UNCERTAINTIES IN DEVELOPINGTHE SITE-SPECIFIC

CLIMATE CHANGE SCENARIO

Martin DubrovskýInstitute of Atmospheric Physics

Prague, Czech Republic

www.ufa.cas.cz/dub/dub.htm

(in Ostravice: “Site-Specific Climate Change Scenarios: Methodology and Uncertainties”)

Motivation • impact models (e.g. crop growth models, rainfall-runoff models)

used in climate change impact analysis require weather series representing changed climate

• 2 methods are often used to produce such series:

• direct modification of observed weather series:- changed climate weather series =

present climate wea series (+/x) climate change scenario

• weather generator (WG):

- changed climate weather series are produced by WG with parameters modified according to the climate change scenario

• climate change scenario is loaded by many uncertainties

Climate change scenario (typical format)

• changes in selected climate characteristics; typically for months:

- TEMP……………………………...additive changes

- PREC, SRAD, WIND, HUMID….multiplicative changes

- std(X)……………………………..multiplicative changes

Construction of GCM-based Climate Change Scenario:

emission scenario

carbon cycle & chemistry model

concentration of GHG and aerosols; radiation forcing

GCM

large-scale patterns of TEMP, PREC, SRAD, …

interpolation

site-specific climate scenario

climate change scenario =

future GCM climate - present GCM climate

sources of uncertaintiesdiscussed in this presentation

Construction of Climate Change Scenario from GCM output

climate change scenario =future climate vs. present climate

problem: GCM simulations have been made only for a limited number of emission scenarios!

solution: pattern scaling technique

( ΔX[tA-tB],month = X[tA-tB],month – Xref,month )

A) “direct” method

or ( ΔX[tA-tB],month = X[tA-tB],month / Xref,month )

B) pattern scaling technique:

where ΔXS = standardised scenario ( = scenario relatedto ΔTG = 1 °C )

a) ΔXS = ΔX[tA-tB] / ΔTG [tA-tB]

b) linear regression [x = ΔTG; y = ΔX] going through zero

ΔTG = change in global mean temperature

assumption: pattern (spatial and temporal /annual cycle/)is constant, only magnitude changes proportionallyto the change in global mean temperature:

!! ΔTG may be estimated by other means than GCMs !!

ΔX(t) = ΔXS x ΔTG(t)

b) uncertainties having an effect on the scaling factor, ΔTG :

ΔTG = MAGICC(emission scenario, climate sensitivity, aerosols)

1. emission scenario: IS92, SRES-98

2. climate sensitivity: ΔTG,2xCO2 = 1.5, 2.5, 4.5 °C

3. aerosols: YES / NO

a) uncertainties having an effect on the pattern of the scenario

1. inter-model uncertainty (7 GCMs)

2. internal GCM uncertainty (4 runs of HadCM2)

3. choice of the site (4 sites in Czechia)

4. determination of the standardised scenario(3 periods + regression technique)

Data

7 AOGCMs (1961-2099, series of monthly means) from IPCC-DDC:• CGCM1 (C) [1990-2100: 1% increase of compound CO2]

• CCSR/NIES (J) [1990 - 2099: IS92a]

• CSIRO-Mk2 (A) [1990-2100: IS92a]

• ECHAM4/OPYC3 (E) [since 1990: IS92a]

• GFDL-R15-a (G) [1958 - 2057: 1 % increase of compound CO2]

• HADCM3 (H) [since 1990: 1% increase of compound CO2; (ensemble of 4 runs)

• NCAR DOE-PCM (N) [bau (~IS92a) since 2000]

4 weather elements: TAVG - daily average temperatureDTR - daily temperature rangePREC - daily precipitation sumSRAD - daily sum of glob.solar radiation

4 exposure units - see the map

inter-model uncertainty(standardised scenario for Czechia; 7 GCMs; TAVG)

inter-model uncertainty(standardised scenario for Czechia; 7 GCMs; PREC)

inter-model uncertainty(standardised scenario for Czechia; 7 GCMs; DTR)

inter-model uncertainty(standardised scenario for Czechia; 7 GCMs; SRAD)

1. inter-model uncertainty- 7 GCMs

2. internal uncertainty of a single GCM

- 4 runs of the HadCM2 ensemble simulations

3. “location error”- 4 exposure units in the Czech Republic

(scenario averaged over 7 GCMs)

Comparison of uncertainties:

uncertainty in TAVG

uncertainty in PREC

uncertainty in DTR

uncertainty in SRAD

4. uncertainty in determining the standardised scenario - validity of the pattern scaling method

pattern scaling ΔX = ΔXS x ΔTG

validity of the pattern scaling technique may be based on assessing the proportionality between ΔX and ΔTG

a) visually• (2010 - 2039) vs (1961 - 1990)• (2040 - 2069) vs (1961 - 1990)• (2070 - 2099) vs (1961 - 1990)• regression (nine 10-yr slices within 2010 - 2099)

b) using the “fit score”

validity of the pattern scaling technique

validity of the pattern scaling technique

uncertainty in standardised scenario

all scenarios should be the same

uncertainty in standardised scenario

all scenarios should be the same

uncertainty in standardised scenario

all scenarios should be the same

uncertainty in standardised scenario

all scenarios should be the same

Table. EPS calculated from 9 standardised scenarios determined from

nine 10-year periods within 2010-2099

validity of “pattern scaling” method(application of the fit score)

CSIRO CGCM ECHAM GFDL HadCM CCSR NCARTAVG 0.03 0.01 0.04 0.07 0.04 0.03 0.06DTR 1.02 0.40 0.93 1.15 1.12 0.93PRE 0.47 1.15 1.46 0.83 0.9 1.25 0.82RAD 0.69 0.81 0.83 0.75 0.39 0.38 0.99

mean squared deviation of dX from the change projected by pattern scalingEPS = mean squared deviation of dX from the average change over the whole period

{ EPS = MSEp / MSE0 = E(ΔX − ΔXsΔTG)2 / E[ΔX − E(ΔX)]2 }

EPS = 0 : perfect fit (ΔX = k. ΔTG)

= 1 : no correlation between ΔX and ΔTG

> 1 : |ΔX| decreases with increasing ΔTG

uncertainties in estimating ΔTG

ΔTG = MAGICC (emiss. scenario, climate sensitivity, aerosols)

a) choice of emission scenario(IS92c, IS92a, IS92e, SRES-B1, SRES-B2, SRES-A1, SRES-A2)

b) climate sensitivity: ΔTG,2xCO2 = 1.5, 2.5, 4.5

°C

c) effect of aerosols: YES / NO

MAGICC:http://www.cru.uea.ac.uk/~mikeh/software/MAGICC_SCENGEN.htm

global mean temperature in 21st century

(effect of emission scenario, climatic sensitivity and aerosols)

(according to MAGICC model)

c o n c l u s i o n s•scenarios are loaded by many uncertainties:

a) “pattern”: GCM > GCM(internal) > interpolation

b) ΔTglob: clim.sensitivity ~ emission scenario > aerosols

•pattern scaling: - uncertainty in standardised scenario differs for individual

characteristics:

- assumptions of the method are valid only for temperature

- rather problematic for PREC, DTR, SRAD

lowest uncertainty in standardised scenario of temperature

don’t use only one scenario in climate change impact studies, but

use a set of scenarios which represent the uncertainties !

final choice of scenario

a) choice of GCM reflects:

- model validation

- various shapes of annual cycle of changes in individual climatic characteristics according to different GCMs

b) ΔTG

- lower / middle / upper estimate

20542100

e.g.: lower: IS92c + low climate sensitivity 0.730.90

middle: IS92a + middle climate sensitivity 1.472.52

upper: IS92e + high climate sensitivity 2.444.71

climate change scenario (CZ; IS92A; 2xCO2 [y=2092]; dTglob = 2.33 deg)

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