DOWNSCALING METHODS FOR CLIMATE RELATED IMPACT ASSESSMENT STUDIES

1

DOWNSCALING METHODS FOR CLIMATE DOWNSCALING METHODS FOR CLIMATE RELATED IMPACT ASSESSMENT STUDIESRELATED IMPACT ASSESSMENT STUDIES

Van-Thanh-Van Nguyen (and Students)Van-Thanh-Van Nguyen (and Students)Endowed Brace Professor Chair in Civil EngineeringEndowed Brace Professor Chair in Civil Engineering

McGill UniversityMcGill UniversityMontreal, Quebec, CanadaMontreal, Quebec, Canada

Brace Centre for Water Resources ManagementBrace Centre for Water Resources ManagementGlobal Environmental and Climate Change CentreGlobal Environmental and Climate Change CentreDepartment of Civil Engineering and Applied MechanicsDepartment of Civil Engineering and Applied MechanicsSchool of EnvironmentSchool of Environment

2December 17, 2007, Singapore Climate Change Symposium

OUTLINEOUTLINE INTRODUCTIONINTRODUCTION

What a hydrologic engineer needs from an What a hydrologic engineer needs from an atmospheric (climate) scientist?atmospheric (climate) scientist? Extreme Precipitation ProcessExtreme Precipitation Process (Extreme Temperature Process)(Extreme Temperature Process)

The “scale” problemThe “scale” problem Climate variability and climate changeClimate variability and climate change

OBJECTIVESOBJECTIVES DOWNSCALING METHODSDOWNSCALING METHODS

Spatial Downscaling IssuesSpatial Downscaling Issues APPLICATIONSAPPLICATIONS

SDSM and LARS-WGSDSM and LARS-WG Some Current DevelopmentsSome Current Developments

CONCLUSIONSCONCLUSIONS


INTRODUCTIONINTRODUCTION Information on rainfall characteristics is Information on rainfall characteristics is essentialessential for for

planning, design, and management of various planning, design, and management of various hydraulic structures (flood protection works, urban hydraulic structures (flood protection works, urban sewers, etc.) sewers, etc.)

Rainfall records by raingages or radar are usually Rainfall records by raingages or radar are usually limitedlimited (< 50 years) and are (< 50 years) and are not sufficientnot sufficient for for assessing assessing reliabilityreliability of hydraulic structure design. of hydraulic structure design.

Stochastic simulation of rainfall processesStochastic simulation of rainfall processes is is neededneeded to generate many long rainfall series. to generate many long rainfall series.

Several rainfall samples of adequate record Several rainfall samples of adequate record length are needed to be able to determine length are needed to be able to determine how how different system designs and operating policies different system designs and operating policies might perform.might perform.

the the variabilityvariability and the and the rangerange of future system of future system performance are better understood, and better performance are better understood, and better system designs and policies could be selected.system designs and policies could be selected.

Extreme Extreme stormsstorms and floods and floods account for more losses account for more losses than any other natural disaster than any other natural disaster (both (both in terms of loss in terms of loss of lives and of lives and economic costs).economic costs).

Damages due to Saguenay flood in Quebec Damages due to Saguenay flood in Quebec (Canada) in 1996: (Canada) in 1996: $800 million dollars.$800 million dollars.

Average annual flood damages in the U.S. are Average annual flood damages in the U.S. are US$2.1 billion dollarsUS$2.1 billion dollars. .

Design RainfallDesign Rainfall = maximum = maximum amount amount of precipitation of precipitation at at a given sitea given site for a specified for a specified durationduration and and return return periodperiod..


……

The choice of an estimation method The choice of an estimation method depends on the availability of historical depends on the availability of historical data:data: Gaged SitesGaged Sites Sufficient long historical Sufficient long historical

records (> 20 years?) records (> 20 years?) At-site MethodsAt-site Methods.. Partially-Gaged SitesPartially-Gaged Sites Limited data Limited data

records records Regionalization MethodsRegionalization Methods.. Ungaged SitesUngaged Sites Data are not available Data are not available

Regionalization MethodsRegionalization Methods. .


Extreme Rainfall Estimation MethodsExtreme Rainfall Estimation Methods At-siteAt-site Frequency Analysis of Precipitation Frequency Analysis of Precipitation

Current practiceCurrent practice: Annual maximum series (AMS) using 2-: Annual maximum series (AMS) using 2-parameter Gumbel/Ordinary moments method, or using 3-parameter Gumbel/Ordinary moments method, or using 3-parameter GEV/ L-moments method.parameter GEV/ L-moments method.

ProblemProblem: Uncertainties in Data, Model and Estimation Method: Uncertainties in Data, Model and Estimation Method RegionalRegional Frequency Analysis of Precipitation Frequency Analysis of Precipitation

Current practiceCurrent practice: GEV/Index-flood method.: GEV/Index-flood method. ProblemProblem: How to define similarity (or homogeneity) of sites?: How to define similarity (or homogeneity) of sites?

4

32

1

Geographically contiguous fixed

regions

Geographically non contiguous fixed

regions

Hydrologic neighborhood type

regions

(WMO Guides to Hydrological (WMO Guides to Hydrological Practices: 1Practices: 1stst Edition 1965 Edition 1965 → → 66th Edition: Section 5.7)th Edition: Section 5.7)


THE “SCALE” PROBLEMTHE “SCALE” PROBLEM The properties of a variable The properties of a variable depend ondepend on the the

scale of measurement or observationscale of measurement or observation.. Are there Are there scale-invariancescale-invariance properties? And properties? And

how to determine these scaling properties?how to determine these scaling properties? Existing methods are limited to Existing methods are limited to the specific the specific

time scaletime scale associated with the data used. associated with the data used. Existing methods Existing methods cannotcannot take into account take into account

the properties of the physical process the properties of the physical process over over different scales.different scales.

Rainfall Estimation Issues (1)Rainfall Estimation Issues (1)


What are the impacts due to the scale What are the impacts due to the scale problem?problem?

On SAMPLING and MEASUREMENTOn SAMPLING and MEASUREMENT Low resolution Low resolution High resolution High resolution

↓↓ AccuracyAccuracy ↑↑ ↓↓ NoiseNoise ↑↑ ↓↓ CostsCosts ↑↑

Optimum resolution?Optimum resolution? On DATA ANALYSIS TECHNIQUEOn DATA ANALYSIS TECHNIQUE

Artifacts due to scale of measurement or Artifacts due to scale of measurement or computation.computation.

Scale-invariance properties?Scale-invariance properties? New techniques?New techniques?


......

On MODELLING TECHNIQUESOn MODELLING TECHNIQUES Scale-invariance models?Scale-invariance models?

The SCALE problem has PRACTICAL The SCALE problem has PRACTICAL and THEORETICAL implications.and THEORETICAL implications. Scale-Invariance (or Scaling) Methods are Scale-Invariance (or Scaling) Methods are

developed in research developed in research ⇒ Engineering ⇒ Engineering Practice?Practice?


Climate Variability and Change Climate Variability and Change will have will have important impacts on important impacts on the hydrologic cycle, the hydrologic cycle, and in particularand in particular the precipitation process the precipitation process!!

How to quantify Climate Change? How to quantify Climate Change? General Circulation Models (GCMs):General Circulation Models (GCMs): A credible simulation of the “A credible simulation of the “averageaverage” “” “large-scalelarge-scale” ”

seasonal distribution of atmospheric pressure, seasonal distribution of atmospheric pressure, temperature, and circulation. (AMIP 1 Project, 31 temperature, and circulation. (AMIP 1 Project, 31 modeling groups)modeling groups)

Climate change simulations from GCMs are Climate change simulations from GCMs are ““inadequateinadequate” for impact studies on regional scales:” for impact studies on regional scales: Spatial resolution ~ 50,000 kmSpatial resolution ~ 50,000 km22

Temporal resolution ~ (daily), month, seasonalTemporal resolution ~ (daily), month, seasonal Reliability of some GCM output variables (such as Reliability of some GCM output variables (such as

cloudiness cloudiness precipitation)? precipitation)?

Rainfall Estimation Issues (2)Rainfall Estimation Issues (2)


…… How to develop How to develop Climate Change scenariosClimate Change scenarios for for

impacts studies in hydrology?impacts studies in hydrology? Spatial scaleSpatial scale ~ a few km ~ a few km22 to several 1000 km to several 1000 km22

Temporal scaleTemporal scale ~ minutes to years ~ minutes to years A A scale mismatchscale mismatch between the information that between the information that

GCM can confidently provide and scales required GCM can confidently provide and scales required by impacts studies.by impacts studies.

““Downscaling methods”Downscaling methods” are necessary!!! are necessary!!!

GCM Climate Simulations

Precipitation at a Local Site


OBJECTIVESOBJECTIVES To review recent progress in To review recent progress in downscaling downscaling

methodsmethods from both from both theoreticaltheoretical and and practicalpractical viewpoints.viewpoints.

To To assess the performance of statistical assess the performance of statistical downscaling methods to find the “downscaling methods to find the “bestbest” ” method method in the simulation of daily precipitation (and extreme temperature) time series for climate change impact studiesclimate change impact studies..

To demonstrate To demonstrate the importance of scaling the importance of scaling considerationconsideration in the estimation of in the estimation of daily daily andand sub-dailysub-daily extreme precipitationsextreme precipitations..


DOWNSCALING METHODSDOWNSCALING METHODS

GCM

RCM or LAM(Dynamic

Downscaling)

Statistical Models

(Statistical Downscaling)

Stochastic Weather

Generators

Regression Models

Weather Typing or Classification

ImpactModels

(Hydrologic Models)

low resolution high resolution1 km

day, hour, minute~ 300 km

month, season, year

Scenarios


(SPATIAL) DYNAMIC DOWNSCALING (SPATIAL) DYNAMIC DOWNSCALING METHODSMETHODS

Coarse GCM + High resolution AGCMCoarse GCM + High resolution AGCM Variable resolution GCM Variable resolution GCM (high resolution over (high resolution over

the area of interest)the area of interest) GCM + RCM or LAM GCM + RCM or LAM (Nested Modeling (Nested Modeling

Approach)Approach) More accurate downscaled results as compared to More accurate downscaled results as compared to

the use of GCM outputs alone. the use of GCM outputs alone. Spatial scales for RCM results ~ 20 to 50 km Spatial scales for RCM results ~ 20 to 50 km

still larges for many hydrologic modelsstill larges for many hydrologic models.. Considerable computing resource requirement.Considerable computing resource requirement.


(SPATIAL) STATISTICAL DOWNSCALING (SPATIAL) STATISTICAL DOWNSCALING METHODSMETHODS

Weather Typing or ClassificationWeather Typing or Classification Generation Generation dailydaily weather series at a local site. weather series at a local site. Classification schemes are somewhat Classification schemes are somewhat subjectivesubjective..

Stochastic Weather GeneratorsStochastic Weather Generators Generation of Generation of realisticrealistic statistical properties of daily weather statistical properties of daily weather

series at a local site.series at a local site. Inexpensive computing resourcesInexpensive computing resources Climate change scenarios based on results predicted by Climate change scenarios based on results predicted by

GCM (GCM (unreliable for precipitationunreliable for precipitation) ) Regression-Based Approaches Regression-Based Approaches

Generation Generation dailydaily weather series at a local site. weather series at a local site. Results limited to local climatic conditions.Results limited to local climatic conditions. Long series of historical data needed.Long series of historical data needed. Large-scale and local-scale parameter relations remain Large-scale and local-scale parameter relations remain valid valid

for future climate conditionsfor future climate conditions.. Simple computational requirementsSimple computational requirements. .


APPLICATIONS APPLICATIONS LARS-WG Stochastic Weather Generator LARS-WG Stochastic Weather Generator (Semenov et al., (Semenov et al.,

1998)1998) Generation of synthetic series of Generation of synthetic series of dailydaily weather data at a local site weather data at a local site

(daily precipitation, maximum and minimum temperature, and daily (daily precipitation, maximum and minimum temperature, and daily solar radiation) solar radiation)

ProcedureProcedure:: Use Use semi-empiricalsemi-empirical probability distributions to describe the state of a day (wet probability distributions to describe the state of a day (wet

or dry).or dry). Use Use semi-empiricalsemi-empirical distributions for precipitation amounts (parameters distributions for precipitation amounts (parameters

estimated for each month).estimated for each month). Use Use normal distributionsnormal distributions for daily minimum and maximum temperatures. for daily minimum and maximum temperatures.

These distributions are conditioned on the wet/dry status of the day. Constant These distributions are conditioned on the wet/dry status of the day. Constant Lag-1 autocorrelation and cross-correlation are assumed.Lag-1 autocorrelation and cross-correlation are assumed.

Use Use semi-empiricalsemi-empirical distribution for daily solar radiation. This distribution is distribution for daily solar radiation. This distribution is conditioned on the wet/dry status of the day. Constant Lag-1 autocorrelation conditioned on the wet/dry status of the day. Constant Lag-1 autocorrelation is assumed.is assumed.


Statistical Downscaling Model (SDSM) Statistical Downscaling Model (SDSM) (Wilby et al., 2001)(Wilby et al., 2001)

Generation of synthetic series of daily weather Generation of synthetic series of daily weather data at a local site based on data at a local site based on empirical empirical relationships between relationships between local-scale predictandslocal-scale predictands (daily temperature and precipitation) and (daily temperature and precipitation) and large-large-scale predictorsscale predictors (atmospheric variables) (atmospheric variables)

Procedure:Procedure: Identify large-scale predictors (X) that could Identify large-scale predictors (X) that could

control the local parameters (Y).control the local parameters (Y). Find a statistical relationship between X and Y.Find a statistical relationship between X and Y. Validate the relationship with independent data.Validate the relationship with independent data. Generate Y using values of X from GCM data.Generate Y using values of X from GCM data.


Some Current DevelopmentsSome Current Developments The Markov Chain, Mixed Exponential The Markov Chain, Mixed Exponential

(MCME) Model for Daily Rainfall:(MCME) Model for Daily Rainfall: Daily rainfall occurrences (First-Order Two-State Daily rainfall occurrences (First-Order Two-State

Markov Chain)Markov Chain)

Daily rainfall amounts (Mixed exponential distribution)Daily rainfall amounts (Mixed exponential distribution)

1for | 1,,, niXjXPp nnnij

kk

kk aa

ap

,01,00

,00,00

kk

kk aa

ap

,11,10

,10,10

)1()()(

2

)(

1

21

xx

epepxf


AN MCME-BASED DOWNSCALING AN MCME-BASED DOWNSCALING METHODMETHOD

AMPs by MCMEAMPs by MCME Downscaled-GCM AMPs by SDSM methodDownscaled-GCM AMPs by SDSM method ww11 + w + w22 = 1 = 1

Minimize Z w1AMPimeanMCME w2AMPi

mean downscaled GCM AMPiobserved

i1

n


A STATISTICAL DOWNSCALING A STATISTICAL DOWNSCALING METHOD USING PRINCIPAL METHOD USING PRINCIPAL COMPONENT REGRESSIONCOMPONENT REGRESSION

n

j

ijjiO1

0

n

j

ijjiA1

0

Oi = precipitation occurrence on day iAi = precipitation amount on day iPij = principal components of predictor climate variablesα , β = parametersε = residual


DATA:DATA: Observed daily precipitation and temperature extremes at Observed daily precipitation and temperature extremes at

four sites in the Greater Montreal Region (Quebec, Canada) four sites in the Greater Montreal Region (Quebec, Canada) for the 1961-1990 period.for the 1961-1990 period.

NCEP re-analysis daily data for the 1961-1990 period.NCEP re-analysis daily data for the 1961-1990 period. CalibrationCalibration: 1961-1975; : 1961-1975; validationvalidation: 1976-1990. : 1976-1990.


EVALUATION INDICESEVALUATION INDICES


Station Lat (o) Long (o) Alt (m) Dorval 45o28’05” -73o44’31” 35.7

Drummondville 45o52’34” -72o28’29” 76.0 Maniwaki 46o18’11” -76o00’36” 192.0

Montreal McGill 45o30’00” -73o34’19” 56.9

Geographical locations of sites under study.Geographical locations of sites under study.

Geographical coordinates of the stations


Dorval

-505

1015

J F M A M J J A S O N D

(mm)

OBS SDSM LARS

The mean of daily precipitation for the period of 1961-1975

BIAS


Dorval

-505

1015


(mm)

OBS SDSM LARS

BIAS

The mean of daily precipitation for the period of 1976-1990


Dorval

-20

0

20

40


(mm)

OBS SDSM LARS

The 90th percentile of daily precipitation for the period of 1976-1990

BIAS


McGill

-10

0

10

20

30


(oC)

OBSERVED vs. SDSM- AND LARS-WG-MEAN OF TMAX

McGill

-100

102030


(oC)

OBS SDSM LARS

The mean of daily tmax for the period of 1976-1990

BIAS


McGill

05

101520253035


(oC)

OBSERVED vs. SDSM- AND LARS-WG-90th PERCENTILE OF TMAX

McGill

-100

10203040


(oC)

OBS SDSM LARS

The 90th percentile of daily tmax for the period of 1976-1990

BIAS


Drummondville

-20-15-10

-505

101520


(oC)

OBSERVED vs. SDSM- AND LARS-WG-MEAN OF TMIN

Drummondville

-20-10

01020


(oC)

OBS SDSM LARS

BIAS

The mean of daily tmin for the period of 1976-1990


Drummondville

-40

-30

-20

-10

0

10

20


(oC)

OBSERVED vs. SDSM- AND LARS-WG-10th PERCENTILE OF TMIN

Drummondville

-40

-20

0

20


(oC)

OBS SDSM LARS

BIAS

The 10th percentile of daily tmin for the period of 1976-1990


GCM and Downscaling Results (Daily Temperature Extremes )

1- Observed2- SDSM [CGCM1]3- SDSM [HADCM3]4- CGCM1-Raw data 5- HADCM3-Raw data

From CCAF Project Report by Gachon et al. (2005)


GCM and Downscaling Results (Precipitation Extremes )

1- Observed2- SDSM [CGCM1]3- SDSM [HADCM3]4- CGCM1-Raw data 5- HADCM3-Raw data

From CCAF Project Report by Gachon et al. (2005)


SUMMARYSUMMARY Downscaling is Downscaling is necessarynecessary!!!!!! LARS-WG and SDSM models could LARS-WG and SDSM models could describe welldescribe well

basic statistical properties of the basic statistical properties of the daily temperature daily temperature extremesextremes at a local site, but at a local site, but both models were unable to reproduce accurately the observed statistics of daily precipitation.

GCM-Simulated Daily Precipitation Series

Daily Extreme Precipitations

Is it feasible?


APPLICATION OF MCME-BASED APPLICATION OF MCME-BASED DOWNSCALING METHODDOWNSCALING METHOD

DATA:DATA: • 30-year daily rainfall record at Dorval Airport (Quebec), Sooke Reservoir (BC), and Roxas City (Philippines) for the 1961-1990 period.• Calibration: 1961-1980• Validation: 1981-1990

Roxas City(2029 mm)

Dorval(897 mm)

Sooke Reservoir(1500 mm)


MCME Model Parameters:MCME Model Parameters:

Seasonal VariabilitySeasonal Variability: Parameters estimated for each : Parameters estimated for each month.month.

Observed frequencies of daily rainfall occurrences for Observed frequencies of daily rainfall occurrences for estimation of estimation of pp00 00 and pand p1010

Maximum likelihood method for estimation of Maximum likelihood method for estimation of p, p, μμ11, and , and μμ22.. Multi-start (MSX) procedureMulti-start (MSX) procedure using the local simplex technique using the local simplex technique

(Nelder and Mead, 1965)(Nelder and Mead, 1965): : A good guess of initial value; otherwise, A good guess of initial value; otherwise, no convergence to optimal solution.no convergence to optimal solution.

Shuffled Complex Evolution (SCE) methodShuffled Complex Evolution (SCE) method (Duan et al., 1993)(Duan et al., 1993): : Random search + local search, more accurate and more robust.Random search + local search, more accurate and more robust.

211000 , ,,, ppp

Estimation of MCME Model ParametersEstimation of MCME Model Parameters


Mixed Exponential Model for Daily Rainfall AmountsMixed Exponential Model for Daily Rainfall Amounts

DorvalDorval

RoxasRoxasCityCity


DorvalDorval

RoxasRoxasCityCity

Transition ProbabilitiesTransition Probabilities


Dorval: MeanDorval: Mean Standard deviationStandard deviation

Roxas City: MeanRoxas City: Mean Standard deviationStandard deviation


PRECIPITATION CHARACTERISTIC

INDEX DEFINITION UNIT

Frequency Prcp1 Percentage of wet days(Threshold of 1 mm)

%

Intensity SDII Simple daily intensity index: sum of daily precipitation divided by the number of wet days

mm/number of wet days

Extremes: Magnitude and Occurrence

CDD Maximum number of consecutive dry days (<1mm)

days

R3days Maximum 3-day precipitation total

mm

Prec90p 90th percentile of rainy amount mm/day

R90N Number of days precipitation exceeds the 90th percentile

days

Physical PropertiesPhysical Properties1: Observed1: Observed2: MCME Model2: MCME Model(100 simulations for(100 simulations forJune-July-August)June-July-August)

DorvalDorval Roxas CityRoxas City


MCMEMCME

HadCM3HadCM3

CGCMCGCM

Calibration: 1961-1980Calibration: 1961-1980


Model 1 Calibration ('61-'80) Validation ('81-'90)

MAE RMSE MAE RMSE

MCME 3.00 3.77 4.35 4.08

HadCM3 3.52 4.94 5.66 4.69

MCME+HadCM3 2.83 3.40 4.23 3.56

Model 2 Calibration ('61-'80) Validation ('81-'90)

MAE RMSE MAE RMSE

MCME 3.00 3.77 4.35 4.08

CGCM 3.16 3.71 3.72 3.01

MCME+CGCM 3.09 3.63 3.81 3.23

HadCM3HadCM3 CGCMCGCM

Validation: 1981-1990Validation: 1981-1990


APPLICATION OF DOWNSCALING USNG APPLICATION OF DOWNSCALING USNG PRINCIPAL COMPONENT REGRESSIONPRINCIPAL COMPONENT REGRESSION


1: Annual PC; 2: Seasonal PC; 3: Stepwise; and 4: SDSM1: Annual PC; 2: Seasonal PC; 3: Stepwise; and 4: SDSM(1976-1990)(1976-1990)


1: Annual PC; 2: Seasonal PC; 3 Stepwise; and 4: SDSM1: Annual PC; 2: Seasonal PC; 3 Stepwise; and 4: SDSM


Daily AMPs estimated from GCMs versus Daily AMPs estimated from GCMs versus observed daily AMPs at Dorval.observed daily AMPs at Dorval.Calibration period: 1961-1975Calibration period: 1961-1975

100

101

102

30

40

50

60

70

80

90

100

Return period (years)

AM D

aily

Prec

ipita

tion

(mm

)

Dist. of AM Daily Precip. before and after adjustment,1961-1975, Dorval

ObservedCGCM2A2Adj-CGCM2A2

100

101

102

30

40

50

60

70

80

90

100

Return period (years)AM

Dail

y Pr

ecipi

tatio

n (m

m)


ObservedHadCM3A2Adj-HadCM3A2

CGCMA2 HadCM3A2


Residual = Daily AMP (GCM) - Observed daily AMP (local)Residual = Daily AMP (GCM) - Observed daily AMP (local)

30 35 40 45 50 55 60 65 70 75 80-2

0

2

4

6

8

10

12

14

16

CGCM2A2 estimates

Resi

dual

s

CGCM2A2 estimates vs Residuals, 1961-1975

ResidualsFitted curve

30 35 40 45 50 55 60 65 70 750

5

10

15

20

25

HadCM3A2 estimatesRe

sidua

ls

HadCM3A2 estimates vs Residuals, 1961-1975

ResidualsFitted curve

Calibration period: 1961-1975Calibration period: 1961-1975

CGCMA2 HadCM3A2


Daily AMPs estimated from GCMs versus Daily AMPs estimated from GCMs versus observed daily AMPs at Dorval.observed daily AMPs at Dorval.Validation period: 1976-1990Validation period: 1976-1990

100

101

102

30

40

50

60

70

80

90

100

Return period (years)

AM D

aily

Prec

ipita

tion

(mm

)


ObservedCGCM2A2Adj-CGCM2A2

100

101

102

30

40

50

60

70

80

90

100

Return period (years)AM

Dail

y Pr

ecipi

tatio

n (m

m)


ObservedHadCM3A2Adj-HadCM3A2

CGCMA2 HadCM3A2Adjusted Daily AMP (GCM) = Daily AMP (GCM) + Residual Adjusted Daily AMP (GCM) = Daily AMP (GCM) + Residual


CONCLUSIONS (1)CONCLUSIONS (1) Significant advancesSignificant advances have been achieved regarding the global climate have been achieved regarding the global climate

modeling. However, GCM outputs are still modeling. However, GCM outputs are still not appropriatenot appropriate for for assessing climate change impacts on the hydrologic cycle.assessing climate change impacts on the hydrologic cycle.

Downscaling methods provide Downscaling methods provide useful toolsuseful tools for this assessment. for this assessment. CalibrationCalibration of the SDSM suggested that:

precipitation was mainly related to zonal velocities, meridional velocities, specific humidities, geopotential height, and vorticity;

tmax and tmin were strongly related to geopotential heights and specific humidities at all levels.

LARS-WG and SDSM models could LARS-WG and SDSM models could describe welldescribe well basic statistical basic statistical properties of the properties of the daily temperature extremesdaily temperature extremes at a local site, but both at a local site, but both models could provide “models could provide “goodgood” but “” but “biasedbiased” estimates of the ” estimates of the observed statistical properties of the daily precipitation process.

The MCME model could describe from The MCME model could describe from good to excellentgood to excellent many many important (statistical and physical) properties of important (statistical and physical) properties of daily daily rainfall time rainfall time series.series.

It is It is feasiblefeasible to link local-scale MCME rainfall extreme simulations with to link local-scale MCME rainfall extreme simulations with large-scale climate variable simulations. large-scale climate variable simulations.


The proposed PC regression models The proposed PC regression models outperformoutperform the SDSM and the SDSM and the stepwise model in the prediction of the mean and standard the stepwise model in the prediction of the mean and standard deviation of the observed series. deviation of the observed series.

The PC regression models are The PC regression models are more accurate more accurate than the SDSM in than the SDSM in reproducing the SDII, R3days and Prec90p for the winter, spring reproducing the SDII, R3days and Prec90p for the winter, spring and autumn seasonsand autumn seasons, and has comparable performance for the summer season and for other indices.

The principal component analysis created The principal component analysis created statistically and statistically and physically meaningful groupingsphysically meaningful groupings of the NCEP predictor of the NCEP predictor variables.variables.

It is It is feasiblefeasible to link to link dailydaily GCM-simulated AMPs with observed GCM-simulated AMPs with observed dailydaily AMPs at a local site using a second-order nonlinear bias- AMPs at a local site using a second-order nonlinear bias-correction function. Hence, the impacts of correction function. Hence, the impacts of climate change for climate change for different scenarios on daily AMPs could be describeddifferent scenarios on daily AMPs could be described..

Choice of the “best” downscaling method requires Choice of the “best” downscaling method requires rigorous rigorous evaluationevaluation (study objectives and region of interest). (study objectives and region of interest).

CONCLUSIONS (2)CONCLUSIONS (2)


......

Thank You!Thank You!


Validation of GCMs for Current Period (1961-1990)Winter Temperature (°C)

Model mean =all flux & non-flux corrected results (vs NCEP/NCAR dataset) [Source: IPCC TAR, 2001, chap. 8]


300k

m

50km

10km

1m

Poin

tGCMs or RCMs supply...

Impact models require ...

A mismatch of scales between what climate models can supply and what environmental impact models require.

Climate Scenario development need: from coarse to high resolution

P. Gachon


I (mm/hr)

time (hr)

I (mm/hr)

time (hr)

True image


UNKNOWN TRUEIMAGE

A

A1A2

ΑΑΑ 21


Climate Trends and VariabilityClimate Trends and Variability1950-19981950-1998

Maximum and minimum temperatures have increased at similar rateMaximum and minimum temperatures have increased at similar rate Warming in the south and west, and cooling in the northeast (winter & spring)Warming in the south and west, and cooling in the northeast (winter & spring)

Trends inFall

Mean Temp(°C / 49 years)

Trends inSpring


Trends inWinter


Trends inSummer


From X. Zhang, L. Vincent, B. Hogg and A. Niitsoo, Atmosphere-Ocean, 2000

DOWNSCALING METHODS FOR CLIMATE RELATED IMPACT ASSESSMENT STUDIES

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