The document discusses uncertainties in modeling precipitation and the hydrological cycle across spatial scales. It focuses on downscaling global climate model projections to local scales using examples from the Karakoram-Himalaya mountains. Several precipitation datasets are characterized, including issues with sparse station coverage, biases, and uncertainties inherent in interpolation methods. Modeling approaches aim to better understand precipitation changes and feed local processes back to large scales while navigating uncertainties at each step of the downscaling chain from global to local.

1. Observa(ons
and
modelling
of
precipita(on
and
the
hydrological
cycle:
uncertain(es
and
downscaling
Jost
von
Hardenberg
-­‐
Elisa
Palazzi
–
Silvia
Terzago
ISAC-­‐CNR,
Torino
with:
L.
Filippi,
P.
Davini,
D.
D’Onofrio
(ISAC-­‐CNR)

2. From
large
to
small
scales
(and
back)
• Climate
projecOons
from
global
climate
models
are
available
at
coarse
resoluOons
(~100
km)
• Climate
change
impacts
act
mostly
at
local
scales
(impacts
on
ecosystems,
hydrology,
risks,
surface
processes)
à
Scale
mismatch
and
need
for
downscaling
• Local
surface
processes
may
feed
back
on
large
scales
à
need
for
upscaling

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downscaling
modeling
chain

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A:/!<#$%&'()*%+L*9.('1!':(*%!The
downscaling
modeling
chain
• All
elements
in
this
chain
are
characterized
by
sources
of
uncertainty

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&-#;7<0"#%#2.-,%):"#-&55&5)
A:/!<#$%&'()*%+L*9.('1!':(*%!The
downscaling
modeling
chain

6. Global
Climate
Models
-­‐ Examples
of
applicaOons
to
precipitaOon
and
snow
depth
in
the
mountains
(Karakoram-­‐Himalaya
and
the
Alps)
using
the
CMIP5
Global
Climate
Models
and
the
EC-­‐Earth
GCM

7. Winter
Westerlies
Indian
summer
Monsoon
ITCZ
(NH
SUMMER)
L
L
HKK
Himalaya
Hindu-­‐Kush
Karakoram
Himalaya:
example
*
Palazzi,
E.,
J.
von
Hardenberg,
and
A.
Provenzale.
2013.
Precipita<on
in
the
Hindu-­‐Kush
Karakoram
Himalaya:
Observa<ons
and
future
scenarios,
J.
Geophys.
Res.
Atmos.,
118,
85–100
*
Filippi,
L.,
Palazzi,
E.,
von
Hardenberg,
J.
&
Provenzale,
A.
2014.
Mul<decadal
Varia<ons
in
the
Rela<onship
between
the
NAO
and
Winter
Precipita<on
in
the
Hindu-­‐Kush
Karakoram.
Journal
of
Climate
(2014).
doi:
10.1175/JCLI-­‐D-­‐14-­‐00286.1,

8. SpaOal
averages
over
the
two
boxes
of
² Gridded
precipitaOon
data
+
reanalyses
² Data
from
GCMs
Annual
cycle
climatology,
long-­‐term
trends,
PrecipitaOon
changes
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1000 2000 3000 4000 5000 6000
m
We
consider
only
the
data
and
model
outputs
from
pixels/grid
points
with
mean
elevaOon
higher
than
1000
m
above
mean
sea
level.
HKK
Himalaya
Approach
HKKH
precipitaOon

9. – In-­‐situ
sta(ons
• CharacterizaOon
of
the
local
condiOons
• Long
temporal
coverage
• Unevenly
distributed,
mainly
in
the
valleys
and
lowland
areas,
leading
to
a
bias
toward
the
lower
elevaOons
• UnderesOmaOon
of
total
precipitaOon
(snow)
– Interpolated
(gridded)
datasets
• Gridding:
reduces
biases
arising
from
the
irregular
staOon
distribuOon
and
is
essenOal
for
the
analysis
of
regional
precipitaOon
trends
• Poor
spaOal
coverage
and
high
sparseness
of
the
underlying
staOons
à
source
of
uncertainty
when
interpolaOng
grid
point
values.
• For
short
averaging
Ome
scales
the
spaOal
intermi^ency
of
precipitaOon
represents
a
major
source
of
uncertainty
for
these
approaches
PrecipitaOon
datasets
&
issues
GPCC
raw
GPCC
interpolated

10. Maximum Number of Gauges/grid over time − CRU
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
0 1 2 3 4 5 6 7 8
gauges/grid
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
Maximum Number of Gauges/grid over time − GPCC
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
0 1 2 3 4 5 6 7 8
gauges/grid
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
Maximum Number of Gauges/grid over time − CRU
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
0 1 2 3 4 5 6 7 8
gauges/grid
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
Maximum Number of Gauges/grid over time − GPCC
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
0 1 2 3 4 5 6 7 8
gauges/grid
70˚
70˚
75˚
75˚
80˚
80˚
85˚
85˚
90˚
90˚
95˚
95˚
25˚ 25˚
30˚ 30˚
35˚ 35˚
Maximum
number
of
gauges/pixel
(1901-­‐2013).
ElevaOon
>
1000
m
a.s.l.
Maximum
number
of
gauges/pixel
(1901-­‐2013).
Time
series
of
the
total
number
of
gauges
in
the
HKK
and
Himalaya
GPCC
raw
GPCC
interpolated
GPCC
Gridded
dataset
PrecipitaOon
datasets
&
issues

11. – Satellite
Data
• SpaOally-­‐complete
coverage
of
precipitaOon
esOmates
• They
do
not
extend
back
beyond
the
1970s
à
not
yet
suitable
for
assessing
long-­‐term
trends
and
for
climatological
studies.
• Problems
in
measuring
snow
accurately
PrecipitaOon
datasets
&
issues
– Merged
in-­‐situ
and
satellite
Datasets
(e.g.,
GPCP)
– Reanalyses
(use
data
assimila(on
techniques
to
keep
the
output
of
a
numerical
model
close
to
observa(ons)
• Reanalysis
data
do
account
for
total
precipitaOon
(rainfall
plus
snow).
• Global
&
conOnuos
data
• Climate
trends
obtained
from
reanalyses
should
be
regarded
with
cauOon,
since
conOnuous
changes
in
the
observing
systems
and
biases
in
both
observaOons
and
models
can
introduce
spurious
variability
and
trends
into
reanalysis
output

12. DATASET
Spatial
domain
Temporal
domain
Spatial
resolution
Temporal
resolution
TRMM
3B42
50°S-50°N 1998-2010 0.25°x0.25° 3-hr
GPCP Global 1979-2010 2.5°x2.5° Monthly
APHRODITE
APHRO_V1003R1
60°E-150°E
15°S-55°S
1951-2007
0.25°x0.25°
Daily
GPCC
V5
Land 1901-2009
0.5°x0.5° Monthly
CRU
TS3.01.01
Land 1901-2009
0.5°x0.5° Monthly
ERA-Interim Global 1979-2011 0.75°x0.75° Daily
EC-Earth GCM Global
1850-2005
+ scenarios
1.125°x1.125° Daily
HKKH
precipitaOon
datasets
*
Palazzi,
E.,
J.
von
Hardenberg,
and
A.
Provenzale.
2013.
Precipita<on
in
the
Hindu-­‐Kush
Karakoram
Himalaya:
Observa<ons
and
future
scenarios,
J.
Geophys.
Res.
Atmos.,
118,
85–
100

13. Summer
precipitaOon
(JJAS),
MulOannual
average
1998-­‐2007
Aphrodite JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
CRU JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
GPCC JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
TRMM JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
GPCP JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
ERA−Interim JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
EC−Earth JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
UncertainOes
in
observaOonal
data
*
Palazzi,
E.,
J.
von
Hardenberg,
and
A.
Provenzale.
2013.
Precipita<on
in
the
Hindu-­‐Kush
Karakoram
Himalaya:
Observa<ons
and
future
scenarios,
J.
Geophys.
Res.
Atmos.,
118,
85–100

14. Aphrodite DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5
mm/day
CRU DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5
mm/day
GPCC DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5
mm/day
TRMM DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5
mm/day
GPCP DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5
mm/day
ERA−Interim DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5
mm/day
EC−Earth DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5
mm/day
Winter
precipitaOon
(DJFMA),
MulOannual
average
1998-­‐2007
*
Palazzi,
E.,
J.
von
Hardenberg,
and
A.
Provenzale.
2013.
Precipita<on
in
the
Hindu-­‐Kush
Karakoram
Himalaya:
Observa<ons
and
future
scenarios,
J.
Geophys.
Res.
Atmos.,
118,
85–100
UncertainOes
in
observaOonal
data

15. Bimodal distribution: similar amplitudes in
the observations → two different large-
scale mechanisms: WWP in the N-NW;
summer monsoon in the S-E part of the
HKK “box”
ERA-Interim and EC-Earth overestimate
total precipitation with respect to the
observations (snow)
Unimodal distribution peaking in July
APHRODITE, TRMM: ~ 5 mm/day
CRU, GPCC: ~ 6.5 mm/day
GPCP: ~ 6 mm/day
ERA-Interim overestimates
precipitation; EC-Earth is in better
agreement with observations
HKK
Himalaya
Annual
cycle
climatology
of
PrecipitaOon

16. PrecipitaOon
Ome
series:
1901-­‐2009

17. http://cmip-pcmdi.llnl.gov/cmip5/
CMIP5 GCMs
Precipitation in the Karakoram-Himalaya: A CMIP5 view 35
Table 1 The CMIP5 models used in this study. Starred entries indicate models with a fully-
interactive aerosol module.
Model ID Resolution Institution ID First/second Key
Lon⇥Lat Lev indirect aerosol e↵ect reference
bcc-csm1-1-m 1.125⇥1.125L26 (T106) BCC No Wu et al (2013)
bcc-csm1-1 2.8125⇥2.8125L26 (T42) BCC No Wu et al (2013)
CCSM4 1.25⇥0.9L27 (T63) NCAR No Meehl et al (2012)
CESM1-BGC 1.25⇥0.9L27 NSF-DOE-NCAR No Hurrell et al (2013)
*CESM1-CAM5 1.25⇥0.9L27 NSF-DOE-NCAR No Hurrell et al (2013)
EC-Earth 1.125⇥1.125L62 (T159) EC-EARTH No Hazeleger et al (2012)
FIO-ESM 2.8125⇥2.8125L26 (T42) FIO No Song et al (2012)
GFDL-ESM2G 2.5⇥2L24 (M45) GFDL No Delworth et al (2006)
GFDL-ESM2M 2.5⇥2L24 (M45) GFDL No Delworth et al (2006)
MPI-ESM-LR 1.875⇥1.875L47 (T63) MPI-M No Giorgetta et al (2013)
MPI-ESM-MR 1.875⇥1.875L95 (T63) MPI-M No Giorgetta et al (2013)
*CanESM2 2.8125⇥2.8125L35 (T63) CCCMA Yes / No Arora et al (2011)
CMCC-CMS 1.875⇥1.875L95 (T63) CMCC Yes / No Davini et al (2013)
CNRM-CM5 1.40625⇥1.40625L31 (T127) CNRM- Yes / No Voldoire et al (2013)
CERFACS
*CSIRO-Mk3-6-0 1.875⇥1.875L18 (T63) CSIRO- Yes / No Rotstayn et al (2012)
QCCCE
*GFDL-CM3 2.5⇥2L48 (C48) GFDL Yes / No Delworth et al (2006)
INM-CM4 2⇥1.5L21 INM Yes / No Volodin et al (2010)
IPSL-CM5A-LR 3.75⇥1.89L39 IPSL Yes / No Hourdin et al (2013)
IPSL-CM5A-MR 2.5⇥1.2587L39 IPSL Yes / No Hourdin et al (2013)
IPSL-CM5B-LR 3.75⇥1.9L39 IPSL Yes / No Hourdin et al (2013)
*MRI-CGCM3 1.125⇥1.125L48 (T159) MRI Yes / No Yukimoto et al (2012)
CMCC-CM 0.75⇥0.75L31 (T159) CMCC Yes / N/A Scoccimarro et al (2011)
FGOALS-g2 2.8125⇥2.8125L26 LASG-CESS Yes / N/A Li et al (2013)
*HadGEM2-AO 1.875⇥1.24L60 MOHC Yes / N/A Martin et al (2011)
*ACCESS1-0 1.875⇥1.25L38 (N96) CSIRO-BOM Yes / Yes Bi et al (2013)
*ACCESS1-3 1.875⇥1.25L38 CSIRO-BOM Yes / Yes Bi et al (2013)
*HadGEM2-CC 1.875⇥1.24L60 (N96) MOHC Yes / Yes Martin et al (2011)
*HadGEM2-ES 1.875⇥1.24L38 (N96) MOHC Yes / Yes Bellouin et al (2011)
*MIROC5 1.40625⇥1.40625L40 (T85) MIROC Yes / Yes Watanabe et al (2010)
*MIROC-ESM 2.8125⇥2.8125L80 (T42) MIROC Yes / Yes Watanabe et al (2011)
*NorESM1-M 2.5⇥1.9L26 (F19) NCC Yes / Yes Bentsen et al (2013)
*NorESM1-ME 2.5⇥1.9L26 NCC Yes / Yes Bentsen et al (2013)
32 GCMs
0.75°-3.75° lon
Aerosols
Fully-Interactive
chemistry
HKKH
PrecipitaOon:
models
*
Palazzi
E.,
J.
von
Hardenberg,
S.
Terzago,
A.
Provenzale.
2014.
Precipita<on
in
the
Karakoram-­‐
Himalaya:
A
CMIP5
view,
Climate
Dynamics,
doi:
10.1007/s00382-­‐014-­‐2341-­‐z

18. CMCC−CM JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
CESM1−CAM5 JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
MIROC5 JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
HadGEM2−ES JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
inmcm4 JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
NorESM1−ME JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
FGOALS−g2 JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
IPSL−CM5A−LR JJAS
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 2 4 6 8 10 12 14 16 18 20
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0.75°
1.25°
1.40°
1.875°
2°
2.5°
2.8125°
3.75°
Spread
between
CMIP5
models
JJAS
precipitaOon
Average
1901-­‐2005
CMIP5
*
Palazzi
E.,
J.
von
Hardenberg,
S.
Terzago,
A.
Provenzale.
2014.
Precipita<on
in
the
Karakoram-­‐
Himalaya:
A
CMIP5
view,
Climate
Dynamics,
doi:
10.1007/s00382-­‐014-­‐2341-­‐z

19. CMCC−CM DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5 6 7 8
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
CESM1−CAM5 DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5 6 7 8
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
MIROC5 DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5 6 7 8
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
HadGEM2−ES DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5 6 7 8
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
inmcm4 DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5 6 7 8
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
NorESM1−ME DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5 6 7 8
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
FGOALS−g2 DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5 6 7 8
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
IPSL−CM5A−LR DJFMA
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0 1 2 3 4 5 6 7 8
mm/day
70˚
70˚
80˚
80˚
90˚
90˚
30˚ 30˚
0.75°
1.25°
1.40°
1.875°
2°
2.5°
2.8125°
3.75°
DJFMA
precipitaOon
Average
1901-­‐2005
CMIP5
Spread
between
CMIP5
models
*
Palazzi
E.,
J.
von
Hardenberg,
S.
Terzago,
A.
Provenzale.
2014.
Precipita<on
in
the
Karakoram-­‐
Himalaya:
A
CMIP5
view,
Climate
Dynamics,
doi:
10.1007/s00382-­‐014-­‐2341-­‐z

20. 36 Elisa Palazzi et al.
Table 2 Multi-annual mean monthly (Jan to Dec) and seasonal (DJFMA and JJAS) values
of the coe cient of variation (CV, the ratio of the multi-model standard deviation to the
multi-model mean expressed as a percentage [%] of the multi-model mean) in the Himalaya
and HKK sub-regions. The time averages are performed over the period 1901-2005 (historical
period) and over the years 2006-2100 for the future scenarios. Please note that the monthly
CV values are obtained from the multiannual mean of montly precipitation from each CMIP5
model shown in Figs. 2a and 2b, while the DJFMA and JJAS CV values are obtained by
averaging in time the seasonal precipitation time series from each model shown in Fig. 3.
(a) (b) (c) (d) (e) (f) (g) (h) (i) (l) (m) (n) (o)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJFMA
Historical
Himalaya 49 39 30 34 38 43 37 32 39 36 47 47 40
HKK 38 32 27 26 38 53 59 53 43 30 30 35 36
RCP 4.5
Himalaya 51 40 30 37 39 42 37 31 39 35 50 56 41
HKK 36 32 30 30 39 53 58 53 42 33 32 34 36
RCP 8.5
Himalaya 51 39 29 39 40 41 37 31 37 37 48 55 40
HKK 36 30 29 31 40 52 57 52 44 33 29 35 37
STD
DEV
SPREAD
MMM
Himalaya
vs
HKK
No
GCM
feature
has
clearly
emerged
as
one
playing
a
key
role
in
providing
the
best
results
in
terms
of
precipita(on
annual
cycle
in
the
two
regions.
Figure 4:
Spread
between
CMIP5
models

21. HKKH
precipita(on:
example
Figure 4:
HIMALAYA
HKK
Palazzi
E.,
J.
von
Hardenberg,
S.
Terzago,
A.
Provenzale.
2014.
Precipita<on
in
the
Karakoram-­‐
Himalaya:
A
CMIP5
view,
Climate
Dynamics,
doi:
10.1007/s00382-­‐014-­‐2341-­‐z
Is
there
one
model,
or
group
of
models,
that
provides
the
best
results
in
terms
of
precipita(on
annual
cycle
in
one
or
both
regions?
Annual
Cycle

22. HKKH
precipita(on:
example
Palazzi
E.,
J.
von
Hardenberg,
S.
Terzago,
A.
Provenzale.
2014.
Precipita<on
in
the
Karakoram-­‐
Himalaya:
A
CMIP5
view,
Climate
Dynamics,
doi:
10.1007/s00382-­‐014-­‐2341-­‐z
HIMALAYA
HKK
Are
there
any
model
features
that
have
emerged
as
those
providing
the
best
results?
Annual
Cycle

23. CMIP5
and
RepresentaOve
concentraOon
pathways
EC-­‐Earth
CMIP5
Global
PrecipitaOon
(RCP
4.5)
RH Moss et al. Nature 463, 747-756 (2010) doi:10.1038/nature08823

24. HKKH
precipita(on:
example
Palazzi
E.,
J.
von
Hardenberg,
S.
Terzago,
A.
Provenzale.
2014.
Precipita<on
in
the
Karakoram-­‐
Himalaya:
A
CMIP5
view,
Climate
Dynamics,
doi:
10.1007/s00382-­‐014-­‐2341-­‐z
CMIP5
Mul(-­‐model
ensemble
Long-­‐term
trends

25. HKKH
precipita(on:
example
Palazzi,
E.,
J.
von
Hardenberg,
and
A.
Provenzale.
2013.
Precipita<on
in
the
Hindu-­‐Kush
Karakoram
Himalaya:
Observa<ons
and
future
scenarios,
J.
Geophys.
Res.
Atmos.,
118,
85–100,
doi:
10.1029/2012JD018697
EC-­‐Earth
mul(-­‐member
ensemble
Long-­‐term
trends

26. Decreasing
snow
depth
trends
in
the
HKKH
from
CMIP5
models
*
Terzago,
S.,
J.
von
Hardenberg,
E.
Palazzi,
and
A.
Provenzale
(2014),
Snowpack
changes
in
the
Hindu-­‐
Kush
Karakoram
Himalaya
from
CMIP5
Global
Climate
Models,
J.
Hydrometeorol.,
15
(6),
2293-­‐2313

27. Snow
depth
in
the
Alps:
example
DJFMA snow depth projections Alps above 1000 m a.s.l.
Year
AverageSND[m]
1850 1900 1950 2000 2050 2100
0.00.20.40.6
CMIP5 ENS HIST
CMIP5 ENS RCP45
CMIP5 ENS RCP85
ERAI Land
CFSR
MERRA
20CRv2
DJFMA snow depth reanalyses Alps above 1000 m a.s.l.
Year
AverageSND[m]
1980 1985 1990 1995 2000 2005
0.00.20.40.6
ERAI Land
CFSR
MERRA
20CRv2
CMIP5 ENS HIST

28. The
EC-­‐Earth
Global
Climate
Model
Ref.:
Hazeleger,
W.
et
al.,
2009.
EC-­‐Earth:
A
Seamless
Earth
System
PredicOon
Approach
in
AcOon.
Bull.
Amer.
Meteor.
ECMWF
IFS
atmosphere
(36r4
–
T255L91/N128)+
H-­‐Tessel
Land/veg
module
+
NEMO
3.3.1
ocean
(ORCA1
L46)
(will
be
3.6)
+
LIM
3
sea
ice
Integrated
Forecast
System
ECMWF
Louvain
La
Neuve
Ice
Model
(LIM2
(ECE
v2)
LIM3
(ECE
v3)
)
H-­‐Tessel
Land-­‐surface
model

29. The
EC-­‐Earth
Global
Earth-­‐System
Model
Ref.:
Hazeleger,
W.
et
al.,
2009.
EC-­‐Earth:
A
Seamless
Earth
System
PredicOon
Approach
in
AcOon.
Bull.
Amer.
Meteor.
Soc.
ECMWF
IFS
atmosphere
(36r4
–
T255L91/N128)+
H-­‐Tessel
Land/veg
module
+
NEMO
3.3.1
ocean
(ORCA1
L46)
(will
be
3.6)
+
LIM
3
sea
ice
+
TM5
chemistry/aerosols
(6°x4°
/
3°x2°)
TM5
atmospheric
chemistry
and
transport
model
Integrated
Forecast
System
ECMWF
Louvain
La
Neuve
Ice
Model
(LIM2
(ECE
v2)
LIM3
(ECE
v3)
)
H-­‐Tessel
Land-­‐surface
model
LPJ-­‐Guess
dynamic
vegetaOon

30. 28
Research
insOtuOons
from
12
different
European
countries.
Technical
support
by
ECMWF.

31. +%#$,%)-%.',/&)'#0&%) 1&2.#3,%)-%.',/&)'#0&%)
*/,454-,%65/#-7,54-)
0#835-,%.32)
)
9':,-/)#3))
&-#;7<0"#%#2.-,%):"#-&55&5)
A:/!<#$%&'()*%+L*9.('1!':(*%!The
downscaling-­‐impact
chain

32. Dynamical
downscaling
of
global
climate
model
outputs
• High
resoluOon
Regional
Climate
Models
nested
into
Global
Climate
Model
outputs
as
part
of
a
downscaling
change
• One-­‐way
nesOng
strategy
(the
RCM
does
not
feed
back
on
the
GCM)
• Advantages
over
stochasOc
and
staOsOcal
downscaling:
realisOc
representaOon
of
– topography
– land
surface
properOes
– Dynamical
and
physical
processes
– Based
on
the
same
equaOons
and
similar
parameterizaOons
as
the
GCMs
• Technique
originally
developed
for
numerical
weather
predicOon,
first
used
for
climate
runs
e.g
by
Giorgi
1990

33. Dynamical
downscaling
with
the
WRF
model
(0.04°
and
0.11°)
Advanced
Research
Weather
and
Forecas(ng
Model
(ARW-­‐WRF),
a
non-­‐
hydrostaOc,
compressible
and
scalar
conserving
state-­‐of-­‐the-­‐art
atmospheric
model
“Perfect
boundary”
experiments:
• Period:
1979-­‐1998
(some
up
to
2008)
• Large
scale
driver:
ERA-­‐Interim
reanalysis
(0.75°
resoluOon).
Provides
BCs
and
iniOal
condiOons
Model
resolu(ons:
0.11°
and
0.37°
• Different
microphysical
schemes:
Thompson,
Morrison,
WSMS6
• Convec(ve
schemes:
Kain-­‐Fritsch,
Be^s-­‐
Miller-­‐Janjic
,
explicit
• ResoluOon:
901x805,
56
verOcal
levels
SimulaOons
@
LRZ/SuperMUC,
Munich

34. ObservaOonal
climatologies
1979-­‐1998

35. WRF
PrecipitaOon
climatology
1979-­‐1998
Kain-­‐Fritsch
-­‐
Thompson
Kain-­‐Fritsch
-­‐
Morrison
Kain-­‐Fritsch
–
WSM6
Be^s-­‐Miller-­‐Janjic
-­‐
Th.
Explicit
0.04°-­‐
Th.
E-­‐OBS.

36. Kain-­‐Fritsch
-­‐
Thompson
Kain-­‐Fritsch
-­‐
Morrison
Kain-­‐Fritsch
–
WSM6
Be^s-­‐Miller-­‐Janjic
-­‐
Th.
Explicit
0.04°-­‐
Th.
E-­‐OBS.
WRF
PrecipitaOon
climatology
1979-­‐1998
Significant
biases,
parOcularly
over
areas
with
complex
topography
….but
we
have
also
to
keep
into
account
uncertainiOes
in
the
observaOonal
datasets
Such
as
significant
underesOmaOon
of
rain-­‐gauge
precipitaOon

37. Summer
precipitaOon
biases
in
the
Alpine
Region
WRF
0.04°/0.11°
vs.
EURO4M
Kain-­‐Fritsch,
0.11°
Be^s-­‐
Miller-­‐Janjic,
0.11°
Explicit
convecOon,
0.04°
KF
Morrison
WSM6
BMJ
0.04°

38. PrecipitaOon
seasonal
cycle
European
domain
Greater
Alpine
Region
Thompson
Morrison
WSM6
BMJ
0.04°
Thompson
Morrison
WSM6
BMJ
0.04°
• The
0.04°
run
with
explicit
convecOon
manages
to
reproduce
JJA
precipitaOon
averages
compaOble
with
observed.
• Different
microphysics
à
no
improvement
in
winter,
role
of
humidity
transport
*
Pieri
A.,
von
Hardenberg
J.,
Parodi
A.,
Provenzale
A.:
Sensi<vity
of
precipita<on
sta<s<cs
to
resolu<on,
microphysics
and
convec<ve
parameteriza<on:
a
case
study
with
the
high-­‐resolu<on
WRF
climate
model
over
Europe,
Journal
of
Hydrometeorology,
sub
judice.

39. What
if
we
compare
with
raingauges?
DistribuOon
of
daily
precipitaOon
intensity
(>
0.1
mm/day)
for
127
staOons
in
TrenOno
1979-­‐1998
Rain
Gauge
0.04°
run

40. +%#$,%)-%.',/&)'#0&%) 1&2.#3,%)-%.',/&)'#0&%)
*/,454-,%65/#-7,54-)
0#835-,%.32)
)
9':,-/)#3))
&-#;7<0"#%#2.-,%):"#-&55&5)
A:/!<#$%&'()*%+L*9.('1!':(*%!The
modeling
chain

41. StochasOc
downscaling:
the
RainFARM
downscaling
procedure
α
Slope
derived
from
P
and
propagated
to
smaller
scales
¨
Belongs
to
the
family
of
“Metagaussian
models”,
based
on
the
nonlinear
transformaOon
of
a
linearly
correlated
process
¨
Uses
simple
staOsOcal
properOes
of
large-­‐scale
meteorological
predicOons
(shape
of
the
power
spectrum)
and
generates
small-­‐scale
rainfall
fields
propagaOng
this
informaOon
to
smaller
scale,
provided
that
the
input
field
shows
a
(approximate)
scaling
behavior
SPATIAL
Power
spectrum
of
rainfall
field
P(X,
Y,
T),
input
field,
reliability
scales
L0,
T0
r(x,y,t),
output
field,
resoluOon
λ,
τ
RAINFarm:
Rainfall
Filtered
Auto
Regressive
Model
*
N.
Rebora,
L.
Ferraris,
J.
von
Hardenberg,
A.
Provenzale
,
2006;
RainFARM:
Rainfall
Downscaling
by
a
Filtered
Autoregressive
Model.
J.
Hydrometeorology,
7,
724-­‐738

42. RainFARM
downscaling:
example
30
km
1
km
PrecipitaOon
field
from
PROTHEUS
StochasOc
realizaOon
of
the
PROTHEUS
downscaled
field,
obtained
with
RainFARM
Example
SON
1958

43. ! )=>>)",.3)2,?2&5)
! )=@AB;>CC=)
! )D,.%<)"&5#%?4#3)
) )
!1EFGHI*()JKLMCN')
)
4 6 8 10 12
41
42
43
44
45
46
47
48
Longitude [°]
Latitude[°]
1
2
3
4
5
6
7
8
9
10
11
! )O%4/?0&)',K()>A>P)')
! )O%4/?0&)'.3()=>Q)')
MM):.K&%5)
DRE3#S".#)&/),%TU))
!"#$%&'()*+*('(,(-%.##
/0.#1234152.#63/5)
0 50 100 150 200 250 300 350
10
−6
10
−4
10
−2
10
0
precipitation [mm/day]
probabilitydensity
Stations
Downscaled PROTHEUS
PROTHEUS
;1#':(&2'!<#$%&'()*%+)
C(*%M3CD)V1,.3S,%%)W.%/&"&0)O?/#)1&2"&55.X&)Y#0&%Z)
*
D'Onofrio,
D.;
Palazzi,
E.;
von
Hardenberg,
J.,
Provenzale
A.,
Calman<
S.;
Stochas<c
Rainfall
Downscaling
of
Climate
Models.
J
of
Hydrometeorology
15
(2),
830-­‐843
(2014)

44. OBSERVATIONS
WRF
RCM
11
km
Downscaled
WRF
1
km
Precipita(on
PDFs
(2003-­‐2014)
WRF
Simula(ons
@
LRZ/SuperMUC
Munich
An
applicaOon
to
the
Upper
Tiber
basin

45. Hydrological
impacts
Observed
vs
modeled
discharges
Gabellani
S,
Boni
G,
Ferraris
L,
von
Hardenberg
J,
Provenzale
A,
Propaga<on
of
uncertainty
from
rainfall
to
runoff:
A
case
study
with
a
stochas<c
rainfall
generator.
Adv.
Water
Resources,
30,
2061-­‐2071
(2007)

46. Open
quesOons
and
perspecOves
• Coupling
of
feedback
at
mul(ple
scales
in
climate
models
(including
local
feedbacks)
is
an
essenOal
step
to
be^er
understand
and
predict
global
climate
changes
• Need
for
mul(-­‐scale
models
to
adequately
address
feedbacks
at
disparate
scales
à
modelling
chain
from
GCMs
to
models
represenOng
local-­‐
vegetaOon
feedbacks
through
downscaling
• Can
we
develop
vegetaOon/land-­‐surface
models
properly
parameterizing
small-­‐
scale
processes
such
as
mulOple
steady
states
of
vegetaOon
?
Rietkerk,
M.
et
al.
(2011)
Ecological
Complexity
8
(3):223-­‐228

47. Equivalent
resoluOon
at
50N:
270
km
135
km
90
km
60
km
40
km
25
km
• Classic
GCMs
too
dependent
on
physical
parameterisaOon
because
of
unresolved
atmospheric
transports
• Role
of
resolved
seaàland
transport
larger
at
high
resoluOon
• Hydrological
cycle
more
intense
at
high
resoluOon
What
changes
with
resoluOon?
The
global
hydrological
cycle
depends
on
model
resolu(on:
Figure
adapted
from
Trenberth
et
al,
2007,
2011
Demory
et
al.,
Clim.
Dyn.,
2013

48. EU’s Horizon 2020
PRIMAVERA: 2015-2020
Overarching objective of PRIMAVERA:
Develop a new generation of well-evaluated high-resolution global
climate models, capable of simulating and predicting regional climate
with unprecedented fidelity.
ISAC-CNR will actively contribute to most of the research activities
proposed in PRIMAVERA:
• development of a process-based metrics tailored for different
regions and seasons.
• assessment of the benefits of increasing model resolution on Pacific
variability and its teleconnection to Europe.
• investigate novel stochastic approaches to represent sub-grid scale
processes
• investigate the climate variability and predictability in the Extra-
Tropics in order to quantify the respective influence of Interdecadal
Pacific Variabiliy, Atlantic Multidecadal Variability and anthropogenic
forcing on recent and future changes in European climate.

49. PRACE project “Climate SPHINX”
CLIMATE Stochastic Physics HIgh resolutioN eXperiments
• 20 Mln core hours on SuperMUC (LRZ),
10th PRACE call, March 2015-February 2016.
• Goal: To investigate the impact stochastic
parameterisations and high resolutions on the
representation of the main features of climate variability
• Experiments with EC-Earth 3.1:
– Coupled experiments at (IFS) T255L91+ (NEMO) ORCA1
– AMIP experiments at T255 (~80 km), T799 (~25 km) and T1279
(~16 km), coupled runs at T255 and T511.
– Sim. period: 1979-2005 (historical) 2040-2070 (RCP 8.5 scenario)
– 3 ensemble members each for stochastic physics and standard
simulations (at T255, T511, T799)
* Weisheimer, A., T.N. Palmer and F. Doblas-Reyes, (2011) Geophys. Res. Lett., 38, L16703
* Dawson, A. and T. N. Palmer (2014), Climate Dyn. doi:10.1007/s00382-014-2238-x
* Dawson, A., T. N. Palmer, and S. Corti (2012) Geophys. Res. Lett., 39, L21805, doi:10.1029/2012GL053284.

50.
ISAC-­‐CNR
will
contribute
in
CRESCENDO
to:
• improvement
of
the
land
surface
component
of
the
EC-­‐
Earth
model,
in
parOcular
the
representaOon
of
ecosystem
mulO-­‐stability,
including
fire
disturbances
and
transiOons
from
forest
to
savannah
in
tropical
and
subtropical
regions.
• invesOgate
impacts
of
land
hydrology
on
climate
(including
vegetaOon-­‐mediated
effects)
in
LS3MIP
experiments
• using
simple
process
models
nested
in
the
outputs
of
the
ESM
runs
as
diagnosOc
tools
EU’s Horizon 2020
CRESCENDO: 2015-2020
Overarching
objec(ve
of
CRESCENDO:
Improve
the
process
realism
and
future
projecOon
reliability
of
European
Earth-­‐
System
Models,
while
evaluaOng
and
documenOng
the
performance
quality
of
these
models
…