1 / 32

UNCERTAINTIES IN DEVELOPING THE SITE-SPECIFIC CLIMATE CHANGE SCENARIO

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.

Download Presentation

UNCERTAINTIES IN DEVELOPING THE SITE-SPECIFIC CLIMATE CHANGE SCENARIO

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. UNCERTAINTIES IN DEVELOPINGTHE SITE-SPECIFICCLIMATE CHANGE SCENARIO (in Ostravice: “Site-Specific Climate Change Scenarios: Methodology and Uncertainties”) Martin Dubrovský Institute of Atmospheric Physics Prague, Czech Republic www.ufa.cas.cz/dub/dub.htm

  2. 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 theclimate change scenario • climate change scenario is loaded by many uncertainties

  3. Climate change scenario (typical format) • changes in selected climate characteristics; typically for months: • TEMP……………………………...additivechanges • PREC, SRAD, WIND, HUMID….multiplicativechanges • std(X)……………………………..multiplicativechanges

  4. 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 uncertainties discussed in this presentation

  5. Construction of Climate Change Scenario from GCM output A) “direct” method climate change scenario = future climate vs. present climate ( ΔX[tA-tB],month = X[tA-tB],month– Xref,month ) or ( ΔX[tA-tB],month = X[tA-tB],month/ Xref,month ) problem: GCM simulations have been made only for a limited number of emission scenarios! solution: pattern scaling technique

  6. B) pattern scaling technique: assumption: pattern (spatial and temporal /annual cycle/) is constant, only magnitude changes proportionally to the change in global mean temperature: ΔX(t) = ΔXSxΔTG(t) whereΔXS = standardised scenario( = scenario related to 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 !! ΔTG may be estimated by other means than GCMs !!

  7. 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) • 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

  8. 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 temperature • DTR - daily temperature range • PREC - daily precipitation sum • SRAD - daily sum of glob.solar radiation • 4 exposure units - see the map

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

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

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

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

  13. Comparison of uncertainties: 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)

  14. uncertainty in TAVG

  15. uncertainty in PREC

  16. uncertainty in DTR

  17. uncertainty in SRAD

  18. 4. uncertainty in determining the standardised scenario - validity of the pattern scaling method pattern scalingΔX = ΔXSx ΔTG • validity of the pattern scaling technique may be based on assessing the proportionality betweenΔXandΔTG • 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”

  19. validity of the pattern scaling technique

  20. validity of the pattern scaling technique

  21. uncertainty in standardised scenario all scenarios should be the same

  22. uncertainty in standardised scenario all scenarios should be the same

  23. uncertainty in standardised scenario all scenarios should be the same

  24. uncertainty in standardised scenario all scenarios should be the same

  25. mean squared deviation of dX from the change projected by pattern scaling EPS = mean squared deviation of dX from the average change over the whole period { EPS = MSEp / MSE0 = E(ΔX − ΔXsΔTG)2 / E[ΔX − E(ΔX)]2 } validity of “pattern scaling” method(application of the fit score) EPS = 0 : perfect fit (ΔX= k. ΔTG) = 1 : no correlation between ΔX and ΔTG > 1 : |ΔX| decreases with increasing ΔTG Table. EPS calculated from 9 standardised scenarios determined from nine 10-year periods within 2010-2099

  26. 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

  27. global mean temperature in 21st century(effect of emission scenario, climatic sensitivity and aerosols) (according to MAGICC model)

  28. 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 !

  29. 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 • 2054 2100 • e.g.: lower: IS92c + low climate sensitivity 0.73 0.90 • middle: IS92a + middle climate sensitivity 1.47 2.52 • upper: IS92e + high climate sensitivity 2.44 4.71

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

  31. E N D

More Related