NARCliM: Regional climate simulations over …ísfb.cat/curriculum/ConDocs/NARCliM_Intro.pdfNARCliM:...

NARCliM: Regional climate simulations over

Australia. Long term analysis

L. Fita

Laboratoire de Météorologie Dynamique, CNRS-UPMC, Jussieu, Paris, France

LMD - Réunion Climat – January 11th 2016, Paris

Contact: [email protected]

– p. 1

Introduction

• Regional climate? But, why?

– p. 2

Introduction


• On the different IPCC reports (www.ipcc.ch):

– p. 2

www.ipcc.ch

Introduction



• Evolution of Horizontal resolutions and complexity of Global Climate Models(GCMs):

[IPCC, AR4, 1997]– p. 2

www.ipcc.ch

Introduction



• Regional climate simulations to improve representation of a given zone [F.

Giorgi, 91, Rev. Geophys.]

– p. 2

www.ipcc.ch

Introduction





− Better representation of the orography/morphology

− Better representation of the atmospheric processes

− Better representation of the small scales

– p. 2

www.ipcc.ch

Introduction





− Better representation of the orography/morphology

− Better representation of the atmospheric processes

− Better representation of the small scales

IPSL-CMIP5 IPSL-CMIP5 EuroCordex (0.44◦)

– p. 2

www.ipcc.ch

Methodology: Regional Climate Simulation

• There is not computational resources to run global climate simulations at

high resolutions (< 10 km)

– p. 3




• GCMs provide global climate evolution

– p. 3





• Regional model to simulate at higher resolution, but smaller region

– p. 3






• Scale of climate simulations. Climate information at low resolution fromGCM, downscaled by the regional model.

GCM → RCM

– p. 3







GCM → RCM

• GCM data: initial and boundary conditions for RCM

– p. 3







GCM → RCM

• GCM data: initial and boundary conditions for RCM

• Expected results:

− Main synoptic climate characteristics: Temperature, jet position, stormtrack... from GCM

− Improvement on representation of extreme events

− Improvement on representation of local phenomena: clouds, seabreeze, convection, fog, katabatic/anabatic winds, ...

– p. 3

NARCliM project

• Collaborative Work with Dr. Daniel Argüeso and A. Prof. Jason P. Evans

– p. 4

NARCliM project


•

http://climatechange.environment.nsw.gov.au/Climate-projections-

– p. 4

http://climatechange.environment.nsw.gov.au/Climate- projections-for-NSW/About-NARCliM

NARCliM project


•


• Regional Climate modeling project for Australia for the NSW and ACT

– p. 4


NARCliM project


•



• Two domains of simulation (50 [CORDEX, AUS44] and 10 Km) with WRF3.3

– p. 4


NARCliM project


•




• Climate uncertainty → Ensemble of 12 members: 4 GCM as forcing + 3

WRF physic configurations. [Evans et al., 2014, Geosci. Model Dev]

– p. 4


NARCliM project


•




• Climate uncertainty → Ensemble of 12 members: 4 GCM as forcing + 3

WRF physic configurations. [Evans et al., 2014, Geosci. Model Dev]

• GCMs and WRF configuration following criteria of independence and

performance [Bishop & Abramowitz, 2013, Clim. Dyn.]

– p. 4


NARCliM project

• Final Ensemble: 4 GCMs x 3 WRF physics

GCMs

MIROC

ECHAM5

CCCMA3.1

MK3.0

×WRF

pbl&sfc cu mp rad

MY J/Eta KF WDM 5 Dudhia/RRTM

MY J/Eta BMJ WDM 5 Dudhia/RRTM

Y SU/MM5 KF WDM 5 CAM/CAM

– p. 5

NARCliM project


GCMs

MIROC

ECHAM5

CCCMA3.1

MK3.0

×WRF

pbl&sfc cu mp rad




• 3 time-windows: 1990-2009, 2020-2039, 2060-2079 and control period withNNRP: 1950-2009

– p. 5

NARCliM project


GCMs

MIROC

ECHAM5

CCCMA3.1

MK3.0

×WRF

pbl&sfc cu mp rad





• Weak spectral nudging [von Storch et al, 2000, Mon. Weather Rev.] winds andgeopotential, down 500 hPa, d01

– p. 5

NARCliM project


GCMs

MIROC

ECHAM5

CCCMA3.1

MK3.0

×WRF

pbl&sfc cu mp rad






• Permanent contact with stake-holders and funding agencies to meet

demands: A list of about 50 variables were finally provided

– p. 5

NARCliM project


GCMs

MIROC

ECHAM5

CCCMA3.1

MK3.0

×WRF

pbl&sfc cu mp rad






• Permanent contact with stake-holders and funding agencies to meet

demands: A list of about 50 variables were finally provided

• A NON-NARCliM experiment at 2 km for the Sydney area was also carriedout (another talk)

– p. 5

NARCliM: Control period results

• L. Fita, J. P. Evans, D. Argüeso, A. King and Y. Li: Evaluation of theregional climate response in Australia to large-scale climate modes in the

historical NARCliM simulations, Climate Dyanmics (2nd revision)

− Analysis of the bias of the 3 WRF physics to gridded observational data,AWAP, [Jones et al., 2009, Aus. Meteoro. Mag.]

− Analysis of the long term climate evolution in comparison with climatemodes

− Analysis based on grid points and in climate regions

– p. 6

Data set-up

• Definition of spatial 14 regions by climate seasonal clustering (k-means,

[Argüeso et al., 2011, J. Climate] ) of pr, tasmin and tasmax

– p. 7

Data set-up



reg. climate reg. climate

1 extreme dry desert 2 alpine temperate

3 tropical 4 desert + tropical storm

5 semi-desert 6 desert + storm

7 temperate wet 8 Mediterranean

9 alpine wet 10 coastal sub-tropical

11 desert 12 tropical

13 tropical 14 mountain sub-tropical

– p. 7

Data set-up



• Masking of the gridded data-based by means of: availability of station datain both space and time

– p. 7

Data set-up



• Masking of the gridded data-based by means of: availability of station datain both space and time

minimum distance of 100km for precipitation and 150km for temperature; minimum period of

data used is 25 years for precipitation and 15 years for temperature; and the minimum

percentage of data present is 20% for precipitation and 10% for temperature

– p. 7

Bias results

− general positive bias all RCMs

− R2 smallest bias

− All over-estimate precipitation along the Great Dividing Range

− R1 and R3 consistently overestimate precipitation in northern Australia,(tropical convection, monsoon). Use of the Kain-Fristch cumulusparameterization. R2 (Betts-Miller-Janjic)

− RCM biases different from reanalysis (NNRP)

– p. 8

Bias results

− R3 different biases than R1 and R2

− R1, R2 southward bias increasing.

– p. 8

Bias results

− RCMs negative bias

− Largest bias in the south-east

− RCMs’ biases independent of those in the driving reanalysis (NNRP).

− No correspondence between more precipitation + cooler temperatures[Power, 1998, Aus Meteo. Mag.] . bias fom intensity? instead of frequency(more cloudiness)?

– p. 8

Bias results

− Seasonal sensitivity only for

NNRP, R2 and in JJA (Winter) for

all models. Equatorwards shift of

the storm track

− Strong overestimation in the

northern tropical region R1 and

R3. (Kain-Fritsch cumulus

scheme too active)

− Small bias along the Great Divid-

ing Range in JJA

– p. 8

Regional results

− 4 representative regions: 7, 8, 9, 13

− Small tendency to over/underestimate in summer/winter

− reg. 8 south-west coast underestimation increases higher amounts (related to storm

tracks)

− reg. 9 western Tasmania, strong topographic influences not adequately captured at 50 km

− Better representation over tropical regions (12, 13) by re-analysis io dry season JJA

– p. 9

Regional results

− Gross features wellrepresented

− R1 and R3 over-estimateinter-annual variability

− RCMs reproduce wetperiods of 1950s or 1970snot seen by re-analyses

− R3 largest differences

– p. 9

Regional results

− Observed warming ten-

dency hotter in RCMs (notappreciable in the NNRP).

– p. 9

LongTerm: correlation results

• Grid point correlations with temporal series of climate modes

Index Period Source

Niño 1+2 1950-2009 NOAA

Niño 3.4 1950-2009 KNMI

IPO 1950-2007 MO-HCCC

SOI 1951-2009 NOAA

DMI 1958-2009 JAMSTEC

Blocking 1950-2009 CCRC, P. Mahler using NOAA 20CR re-analysis

SAM 1957-2009 BAS

– p. 10


SON pracc SON tasmin SON tasmax

− For pracc RCM larger area, NNRP shows weaker correlations at East.

− tasmin dipole pattern south-west the north-east

− tasmax less significant and models generally fail negative correlation East

− Rmean shows a better correlation map except for precipitation with a clear stronger and

wider positive correlation– p. 10


SON pracc SON tasmin SON tasmax

− Only results for SON (largest correlations)

− SOI, niño 3.4 and blocking highest correlations

− RCMs better regional correlations with precipitation.

− RCMs overestimate corr. tasmin underestimate tasmax

– p. 10


SOI pracc SOI tasmin SOI tasmax

− Maximum correlated index grid-point map (SON, only)

− Precipitation kind of correct, but different maps

− Over dependence on the DMI index in certain parts

– p. 10

Conclusions

• RCMs tend to generally improve climate representation in a complexclimate region as Australia, but not all aspects

– p. 11

Conclusions


• Long term simulations allow to analyze long term climate characteristics

– p. 11

Conclusions



• Good RCM spread following independence-performance

– p. 11

Conclusions




• Some RCMs results even better than re-analysis (model, variable, region

and seasonal dependency)

– p. 11

Conclusions






• Complementary information about RCM performance: better representationof teleconnections with climate modes in many cases, but wrongly in others

– p. 11

Conclusions






• Complementary information about RCM performance: better representationof teleconnections with climate modes in many cases, but wrongly in others

Thank you for your attention!!– p. 11

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