Trento june2015 slides

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)

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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•  All elements in this chain are characterized by sources of uncertainty

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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

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,

SpaOal averages over the two boxes of ² Gridded precipitaOon data + reanalyses ² Data from GCMs

Annual cycle climatology, long-‐term trends, PrecipitaOon changes

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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

–  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

Maximum Number of Gauges/grid over time − CRU

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Maximum Number of Gauges/grid over time − CRU

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Maximum Number of Gauges/grid over time − GPCC

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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

GPC

C Grid

ded da

taset

PrecipitaOon datasets & issues

–  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

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

Summer precipitaOon (JJAS), MulOannual average 1998-‐2007 Aphrodite JJAS

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0 2 4 6 8 10 12 14 16 18 20mm/day

CRU JJAS

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0 2 4 6 8 10 12 14 16 18 20mm/day

GPCC JJAS

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TRMM JJAS

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GPCP JJAS

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ERA−Interim JJAS

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EC−Earth JJAS

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UncertainOes in observaOonal data


Aphrodite DJFMA

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GPCC DJFMA

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EC−Earth DJFMA

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Winter precipitaOon (DJFMA), MulOannual average 1998-‐2007


UncertainOes in observaOonal data

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

PrecipitaOon Ome series: 1901-‐2009

http

://c

mip-p

cmdi

.llnl.go

v/cm

ip5/

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 KeyLon⇥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

CMCC−CM JJAS

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0 2 4 6 8 10 12 14 16 18 20

mm/day70˚

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90˚

30˚ 30˚

CESM1−CAM5 JJAS

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90˚

30˚ 30˚

0 2 4 6 8 10 12 14 16 18 20

mm/day70˚

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MIROC5 JJAS

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HadGEM2−ES JJAS

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inmcm4 JJAS

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NorESM1−ME JJAS

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90˚

30˚ 30˚

FGOALS−g2 JJAS

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0 2 4 6 8 10 12 14 16 18 20

mm/day70˚

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90˚

30˚ 30˚

IPSL−CM5A−LR JJAS

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90˚

30˚ 30˚

0 2 4 6 8 10 12 14 16 18 20

mm/day70˚

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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


CMCC−CM DJFMA

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CESM1−CAM5 DJFMA

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MIROC5 DJFMA

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90˚

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mm/day70˚

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HadGEM2−ES DJFMA

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80˚

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90˚

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0 1 2 3 4 5 6 7 8

mm/day70˚

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80˚

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90˚

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inmcm4 DJFMA

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80˚

90˚

90˚

30˚ 30˚

0 1 2 3 4 5 6 7 8

mm/day70˚

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80˚

90˚

90˚

30˚ 30˚

NorESM1−ME DJFMA

70˚

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80˚

80˚

90˚

90˚

30˚ 30˚

0 1 2 3 4 5 6 7 8

mm/day70˚

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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/day70˚

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30˚ 30˚

IPSL−CM5A−LR DJFMA

70˚

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80˚

80˚

90˚

90˚

30˚ 30˚

0 1 2 3 4 5 6 7 8

mm/day70˚

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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



36 Elisa Palazzi et al.

Table 2 Multi-annual mean monthly (Jan to Dec) and seasonal (DJFMA and JJAS) valuesof the coe�cient of variation (CV, the ratio of the multi-model standard deviation to themulti-model mean expressed as a percentage [%] of the multi-model mean) in the Himalayaand HKK sub-regions. The time averages are performed over the period 1901-2005 (historicalperiod) and over the years 2006-2100 for the future scenarios. Please note that the monthlyCV values are obtained from the multiannual mean of montly precipitation from each CMIP5model shown in Figs. 2a and 2b, while the DJFMA and JJAS CV values are obtained byaveraging 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) (p)Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJFMA JJAS

HistoricalHimalaya 49 39 30 34 38 43 37 32 39 36 47 47 40 37HKK 38 32 27 26 38 53 59 53 43 30 30 35 36 52

RCP 4.5Himalaya 51 40 30 37 39 42 37 31 39 35 50 56 41 36HKK 36 32 30 30 39 53 58 53 42 33 32 34 36 51

RCP 8.5Himalaya 51 39 29 39 40 41 37 31 37 37 48 55 40 35HKK 36 30 29 31 40 52 57 52 44 33 29 35 37 51

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:


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


Figure 4:


HIMALAYA HKK

Are there any model features that have emerged as those providing the best results?

Annual Cycle

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



CMIP5 Mul(-‐mod

el ensem

ble

Long-‐term trends


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 m

ul(-‐mem

ber e

nsem

ble

Long-‐term trends

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

Snow depth in the Alps: example DJFMA snow depth projections � Alps above 1000 m a.s.l.

Year

Ave

rage

SN

D[m

]

1850 1900 1950 2000 2050 2100

0.0

0.2

0.4

0.6

CMIP5�ENS�HISTCMIP5�ENS�RCP45CMIP5�ENS�RCP85ERAI�LandCFSRMERRA20CRv2

DJFMA snow depth reanalyses � Alps above 1000 m a.s.l.

Year

Ave

rage

SN

D [m

]

1980 1985 1990 1995 2000 2005

0.0

0.2

0.4

0.6

ERAI�LandCFSRMERRA20CRv2CMIP5�ENS�HIST

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

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

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

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A:/!<#$%&'()*%+L*9.('1!':(*%!The downscaling-‐impact chain

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

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

ObservaOonal climatologies 1979-‐1998

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.

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

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°

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.

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

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StochasOc downscaling: the RainFARM downscaling procedure

α Slope derived from P and propagated to smaller scales

¨  Be l ong s t o t he f am i l y o f “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) scal ing 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

RainFARM downscaling: example

30 km 1 km

PrecipitaOon field from PROTHEUS StochasOc realizaOon of the PROTHEUS downscaled field, obtained with RainFARM

Example SON 1958

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43

44

45

46

47

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Longitude [°]

Latit

ude [

°]

1

2

3

4

5

6

7

8

9

10

11

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* 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)

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

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)

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

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 seaàland 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

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.

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.

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 …

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