Atmospheric Warming and the Amplification of Precipitation Extremes
Projected changes to precipitation extremes for Northeast...
Transcript of Projected changes to precipitation extremes for Northeast...
Projected changes to precipitation extremes for Northeast Canadian watersheds using a multi-RCM
ensemble
CMOS-Montréal May 30th 2012
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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