uncertainties in developing the site-specific climate change scenario
DESCRIPTION
UNCERTAINTIES IN DEVELOPING THE SITE-SPECIFIC CLIMATE CHANGE SCENARIO. (in Ostravice: “Site-Specific Climate Change Scenarios: Methodology and Uncertainties”). Martin Dubrovsk ý Institute of Atmospheric Physics Prague, Czech Republic. www.ufa.cas.cz/dub/dub.htm. Motivation. - PowerPoint PPT PresentationTRANSCRIPT
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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”)
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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
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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
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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
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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 )
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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)
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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)
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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
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inter-model uncertainty(standardised scenario for Czechia; 7 GCMs; TAVG)
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inter-model uncertainty(standardised scenario for Czechia; 7 GCMs; PREC)
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inter-model uncertainty(standardised scenario for Czechia; 7 GCMs; DTR)
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inter-model uncertainty(standardised scenario for Czechia; 7 GCMs; SRAD)
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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:
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uncertainty in TAVG
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uncertainty in PREC
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uncertainty in DTR
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uncertainty in SRAD
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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”
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validity of the pattern scaling technique
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validity of the pattern scaling technique
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uncertainty in standardised scenario
all scenarios should be the same
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uncertainty in standardised scenario
all scenarios should be the same
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uncertainty in standardised scenario
all scenarios should be the same
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uncertainty in standardised scenario
all scenarios should be the same
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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
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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
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global mean temperature in 21st century
(effect of emission scenario, climatic sensitivity and aerosols)
(according to MAGICC model)
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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 !
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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
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climate change scenario (CZ; IS92A; 2xCO2 [y=2092]; dTglob = 2.33 deg)
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