Projected changes to precipitation extremes for Northeast Canadian watersheds using a multi-RCM

ensemble

CMOS-Montréal May 30th 2012

1

André Monette1, Laxmi Sushama1, Naveed Khaliq2, René Laprise1 and René Roy3

1) UQÀM, centre ESCER 2) U. of Saskatchewan, School of Environment and Sustainability 3) Hydro-Québec, Ouranos

OUTLINE

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1) Motivation • Precipitation extremes in the context of changing

climate.

2) Models and simulations • NARCCAP simulations • Observed dataset • Precipitation characteristics and watersheds considered

3) Results • Quantification of uncertainties associated with RCMs • Projected changes: comparison of future 2041-2070

and current 1971-2000 period return levels

4) Conclusions

Motivation Why study extremes?

Consequences Economic Social Environmental

Saguenay 1996 3

Climate change Temperature Water-holding capacity

Climate variability Precipitation extremes

Hydro-electricity in Québec

96% of the energy produced comes from Hydro-electricity

97% of the new energy produced in the next decade will be held in the territory associated with the Plan Nord (86% for hydro-electricity)

http://plannord.gouv.qc.ca/potentiel/energetiques.asp

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Motivation

Important implications for water resources for better management and planning of reservoirs in the region

Why Québec?

Why multi-models? Advantages :

Structural uncertainties (resolution, parameterization, domain, physical process)

Lateral boundary forcing uncertainties

Scenarios uncertainties

Quantification and assessment

Example of project using ensemble models:

PRUDENCE (Europe): • Ensemble of 8 RCMs et 4 GCMs • 2071-2100 • Many resolutions/scenarios

ENSEMBLE (Europe et Africa): • Up to 16 RCM et 7 GCM • Many periods • Many resolutions and scenarios

NARCCAP (North America)

Models and data

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GCM RCM

GFDL (NOAA-GFDL)

CGCM3 (CCCMA)

HADCM3

(Hadley Centre)

CCSM3 (NCAR) NCEP

MM5I (Iowa State University)

X X X

RCM3 (UC Santa

Cruz) X X X

CRCM (Ouranos/U

QAM) X X X

HRM3 (Hadley Centre)

X X X

WRFG (Pacific

Northwst Nat’l Lab)

X X X

ECP2 (Experimental Climate Prediction Center—regional spectral model)

X X X

6 X : unavailable simulation X : available simulation

Models and data

RCMs’ domain

• 6 RCMs • 4 AOGCMs used to drive the RCMs • NCEP reanalysis used to drive the RCMs

What is NARCCAP? North American Regional Climate

Change Assessment Program

Reference grid :

The 21 watershed

studied

In green Observed gridded dataset (10km res)

(D. Tapsoba – Hydro-Québec) Slivitzky et al. 2005 7

Reference grid and studied region/watersheds Models and data

NARCCAP RCMs used distinct horizontal grid (same resolution but different projection)

Extreme precipitation characteristics

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d: Duration: 1-, 3- and 7-day n: Return periods: 10-, 30- and 50-year

Q(d,n) Return levels:

Models and data

Study period: May to October Avoid mixed snow/rain precipitation Maintain the uniqueness in physical proprieties

Evaluation of RCMs Projected changes 1980-2000 period Current period (1971-2000)

Approach: Regional frequency analysis (Hosking and Wallis 97)

6 RCM_NCEP pairs Future period (2041-2070) Following A2 scenario (IPCC)

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Methodology

RCMs’ performance errors

Observation vs RCM_NCEP %

Lateral boundary forcing errors

RCM_GCM vs RCM_NCEP %

RCMs’ structural uncertainties 6 RCM_NCEP CV

RCMs’ uncertainties – choice of GCM

RCM_GCM(1) vs RCM_GCM(2) CV

Evaluation of RCMs, uncertainties and projected changes

Projected changes Relative change 2041-2070 vs 1971-2000 %

Statistical significance of projected changes

Vector Bootstrap approach

95% confidence level

Confidence in projected changes 8 RCM_GCM CV

PART 1:

PART 2:

Observed return levels –

Return levels decrease from south to north

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Q(d,n)

RCMs’ performance errors

Observation vs RCM_NCEP

Lateral boundary forcing errors

RCM_GCM vs RCM_NCEP

RCMs’ structural uncertainties 6 RCM_NCEP

RCMs’ uncertainties – choice of GCM

RCM_GCM(1) vs RCM_GCM(2)

PART 1

CRCM underestimates observed return levels

ECP2 et HRM3 overestimate observed return levels

Northern watersheds = overestimation

Observed vs RCM_NCEP return levels Q(1,10)

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Assessment of RCMs’ performance errors

PART 1 RCMs’ performance errors

Observation vs RCM_NCEP

Lateral boundary forcing errors

RCM_GCM vs RCM_NCEP

RCMs’ structural uncertainties 6 RCM_NCEP

RCMs’ uncertainties – choice of GCM

RCM_GCM(1) vs RCM_GCM(2)

10-yr

1-day 3-day 7-day

30-yr

50-yr

CV of the 6 RCM_NCEP return levels Q(d,n)

Less uncertainties for northern watersheds

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RCMs’ structural uncertainties RCMs’ performance errors

Observation vs RCM_NCEP

Lateral boundary forcing errors

RCM_GCM vs RCM_NCEP

RCMs’ structural uncertainties 6 RCM_NCEP

RCMs’ uncertainties – choice of GCM

RCM_GCM(1) vs RCM_GCM(2)

PART 1

3-day 7-day 1-day

10-yr

30-yr

50-yr

%

Mean = 13% South-East = 5 à 9% North= 15 à 19% 13

2041-2070 vs 1971-2000 return levels Q(d,n) Projected changes Projected changes

2041-2070 vs 1971-2000

Relative changes (%)

Significant changes Vector bootstrap approach

Confidence in projected changes CV

PART 2

Ensemble of 8 RCM_GCM pairs

3-day 7-day 1-day

10-yr

30-yr

50-yr

8 7 6

5 4 3 2

1

0

Number of simulation pairs that predict significant changes (95% confidence level)

Significant increases for 10-year return period 14

/maximum of 8 simulations

Projected changes 2041-2070 vs 1971-2000

Relative changes (%)

Significant changes Vector bootstrap approach

Confidence in projected changes CV

PART 2 Projected changes

Conclusions Northern watersheds

Largest projected changes/ High confidence/ Significant changes found for many simulations

Southeastern watersheds Smallest projected changes / Low confidence / Significant changes found for few simulations

Ideally… More GCM – RCM pairs (presently max of 2 by RCM)

Improved observed dataset covering the entire domain

Ensemble-averaged appear to be more representative, compared to individual models

Multi-RCM ensemble

Allowed quantification of uncertainties in the result

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More scenarios

High resolution

References: • http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-spm-fr.pdf • http://www.iea.org/index_info.asp?id=1959 • http://www.geovariances.com/en/IMG/pdf/Abstract-GeoEnv2010-

JeanneeTapsoba.pdf • http://plannord.gouv.qc.ca/ • http://www.narccap.ucar.edu/ • Beniston, M., D. B. Stephenson, O. B. Christenson, C. A. T. Ferro, C. Frei, S.

Goyette, K. Halsnaes, T. Holt, K. Jylhä, B. Koffi, J. Palutikof, R. Schöll, T. Semmler, and K. Woth (2007), Future extreme events in European climate: an exploration of regional climate model projections, Climatic Change, 81, 71–95.

• Davison, A. C., and D. V. Hinkley (1997), Bootstrap methods and their application, Cambridge University Press, Cambridge, UK. 582 pp.

• de Elía R, D. Caya, H. Côté, A. Frigon, S. Biner, M. Giguère, D. Paquin, R. Harvey, D. Plummer (2008), Evaluation of uncertainties in the CRCM-simulated North American climate, Clim. Dyn., 30, 113-132. doi:10.1007/s00382-007-0288-z.

• Efron, B., and R. J. Tibshirani (1993), An introduction to the bootstrap, Chapman and Hall, London, UK. 436 pp.

• Ekström, M., H.J. Fowler, C.G. Kilsby, and P. D. Jones (2005), New estimates of future changes in extreme rainfall across the UK using regional climate model integrations. 2. Future estimates and use in impact studies, J. Hydrol., 300(1–4), 234–251.

• Emori, S., A. Hasegawa, T. Suzuki, and K. Dairaku (2005), Validation, parameterization dependence and future projection of daily precipitation simulated with a high-resolution atmospheric GCM, Geophys. Res. Let., 32, L06708, doi:10.1029/2004GL022306.

• Emori S, SJ. Brown (2005), Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate, Geophys. Rev. Lett. 32, L17706, doi:10.11029/2005GL023272.

References • Fowler, H. J., and M. Ekström (2009), Multi-model ensemble estimates of

climate change impacts on UK seasonal rainfall extremes, Int. J. Climatol., 29, 385–416, doi:10.1002/joc.1827. Frei, C., R. Schöoll, S. Fukutome, J. Schmidli, and P. L. Vidale (2006), Future change of precipitation extremes in Europe: Intercomparison of scenarios from regional climate models, J. Geophys. Res., 111, D06105, doi:10.1029/2005JD005965.

• Frigon, A., B. Music, and M. Slivitzky (2010), Sensitivity of runoff and projected changes in runoff over Quebec to the update interval of lateral boundary conditions in the Canadian RCM, Meteorologische Zeitschrift, 19(3): 225-236, doi:10.1127/0941-2948/2010/0453.

• Gutowski, J. William, and Coauthors (2010) Regional Extreme Monthly Precipitation Simulated by NARCCAP RCMs, J. Hydrometeor, 11, 1373–1379, doi:10.1175/2010JHM1297.1

• Hosking, J. R. M., and J. R. Wallis, 1997: Regional Frequency Analysis. Cambridge University Press, 224 pp.

• Mladjic, B., L. Sushama, M. N. Khaliq, R. Laprise, D. Caya, R. Roy (2011), Canadian RCM Projected Changes to Extreme Precipitation Characteristics over Canada, J. Climate, 24, 2565–2584.

• Tebaldi C., J. M. Arblaster, K. Hayhoe, and G. A. Meehl (2006), Going to the extremes: An intercomparison of model-simulated historical and future changes in extreme events, Climatic Change, 79, 185–211.

• Zwiers, F. W., and V. V. Kharin (1998), Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling, J. Climate, 11, 2200–2222

Thank you!!!!

10-yr

30-yr

50-yr

1-day 3-day 7-day %

Mean = 20-22% South-East = 15-18% North= 20-25% 13

Q(d,n) 2041-2070 vs Q(d,n)1971-2000 Projected changes - Winter

Result Rcms performance errors

Observation vs RCM_NCEP

RCMs structural uncertainties 6 RCM_NCEP

Projected changes 2041-2070 vs 1971-2000

Relative changes (%)

Significant changes Vector bootstrap approach

10-yr

30-yr

50-yr

1-day 3-day 7-day

8 7 6

5 4 3

2

1

0

Number of simulation pairs that predict a significant change (95% confidence level)

Result

Significant increases for 10-year return period

More variability compared to Q(1,10)

Ensemble: Nearly half of the RCMs underestimate the return values.

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Q(7,50) observation vs Q(7,50) MRC_NCEP

Rcms performance errors

Observation vs RCM_NCEP

RCMs structural uncertainties 6 RCM_NCEP

Projected changes 2041-2070 vs 1971-2000

Relative changes (%)

Significant changes Vector bootstrap approach

Assessment of RCMs Result

10-yr

1-day 3-day 7-day

30-yr

50-yr

Largest differences are found for southeastern watersheds 21

CV of Q(d,n) CRCM_CGCM3 and CRCM_CCSM Uncertainties – Choice of GCMs (boundaries)

Result Rcms performance errors

Observation vs RCM_NCEP

RCMs structural uncertainties 6 RCM_NCEP

Projected changes 2041-2070 vs 1971-2000

Relative changes (%)

Significant changes Vector bootstrap approach

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Methodology

RCMs performance errors

Observation vs RCM_NCEP % Q(1,10) & Q(7,50)

RCMs structural uncertainties 6 RCM_NCEP CV Q(d,n)

Projected changes(2041-2070)

Relative change between 2041-2070 and 1971-

2000

% Ensemble of 8 pairs RCM_GCM

Statistical significance of projected changes

Vector Bootstrap approach /8 95% confidence

level

Coefficient de variation: σ : Standard deviation of the

return levels μ : Average of the return levels

Evaluation of RCMs, uncertainties and projected changes

30 ans

50 ans

10 ans

1 jr 3 jrs 7 jrs

0 < Cv ≤ 0.5

1.0 < Cv ≤ 1.5

Cv > 1.5

0.5 < Cv ≤ 1.0

Confiance dans les changements appréhendés

Grande incertitude avec les bassins au sud-est du Qc 26

ΔQ(d,n) = Q(d,n)fut - Q(d,n)cour

Changements appréhendés (2041-2070)

Différence relative entre 2041-2070 et 1971-2000

Changements significatifs Méthode du vecteur Bootstrap

Identification des régions avec une grande confiance

CV des changements appréhendés

Résultat

7 jrs

3 jrs

1 jrs

Plus grande variabilité pour les longues périodes de retour (vert)

CRCM_CGCM3

7.1% 9.0%

10.2%

8.9% 9.3% 9.5%

7.4% 10.0% 11.6%

ECP2_GFDL

18.0% 18.8% 18.8%

19.7% 20.5% 20.8%

21.4% 21.1% 21.0%

HRM3_HADCM3

14.6% 16.0% 17.2%

14.5% 15.8% 16.2%

11.4% 12.7% 13.6%

WRFG_CCSM

6.8% 8.1% 9.2%

4.6% 6.1% 6.9%

3.7% 5.0% 5.9%

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10 ans

30 ans

50 ans

Q(d,n) MRC_NCEP vs Q(d,n) MRC_MGC

Erreur de la performance des MRC

Observation vs MRC_NCEP

Erreur liée au choix des conditions aux frontières

MRC_MCG vs MRC_NCEP

Incertitude liée à la structure des MRC 6 MRC_NCEP

Incertitude des MRC liée au choix des conditions aux frontières du MCG

MRC_MCG(1) vs MRC_MCG(2)

NCEP

MGC

Choix des conditions aux frontières Résultat