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Funded under the European Commission Seventh Framework Programme Contract Number: 244031

Climate change integrated assessment methodology for cross- sectoral adaptation and vulnerability in Europe. Climate change scenarios incorporated into the CLIMSAVE Integrated Assessment Platform. For further information contact Martin Dubrovsky (email: ma.du@seznam.cz)

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Funded under the European Commission Seventh Framework Programme Contract Number: 244031

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  1. Climate change integrated assessment methodology for cross-sectoral adaptation and vulnerability in Europe Climate change scenarios incorporated into the CLIMSAVE Integrated Assessment Platform For further information contact Martin Dubrovsky (email: ma.du@seznam.cz) or visit the project website (www.climsave.eu) Funded under the European Commission Seventh Framework Programme Contract Number: 244031

  2. Presentation structure 1. Introduction 2. Methodologies for preparing reduced-form ensembles of future climate scenarios (...focus on uncertainties) 2.1 GCM ensemble (CMIP3 data ~ IPCC-AR4) for European case study 2.2 UKCP09data for Scottish case study + representativeness of the reduced-form ensembles 3. Comparison of GCM-based vs. UKCP09 scenarios 4. Summary & Conclusion

  3. Introduction – CLIMSAVE project CLIMSAVE project (www.climsave.eu; 2010-2013) • coordinated by the Environmental Change Institute, University of Oxford • 18 partnersfrom 13 countries (incl. China and Australia) • Aim:integrated methodology to assess cross-sectoral climate change impacts, adaptation and vulnerability  • The main product of CLIMSAVE:a user-friendly, interactive web-based tool (Integrated Assessment Platform; IAP) that will allow stakeholders to assess climate change impacts and vulnerabilities for a range of sectors • IAP is based on an ensemble of meta-models, which are run with the user-selected climatic datarepresenting present and future climates • When creating an ensemble of climate change scenariosfor the IAP, two requirements were followed: 1. an ensemble of climate change scenarios is not large, and 2. it satisfactorily represents known uncertainties in future climate projections.

  4. GCM-based scenarios(based on monthly GCM outputsfrom IPCC-AR4 database /~CMIP3/;Europe)

  5. GCMs in CMIP3 database We use 16 SRES-A2 simulations of 24 GCMs x 6 emission scenarios (incomplete matrix).

  6. Pattern scaling is used to create a set of climate change scenarios Pattern scaling approach allows to reflect multiple uncertainties:- where several ΔTG values are used to multiply several GCM-based patterns ΔTG = change in global mean temperature ΔXS = standardised scenario (relatedto ΔTG= 1K; derived from GCMs) ΔX(t) = ΔXSx ΔTG(t) uncertainty in pattern (~ modelling uncertainty): uncertainty in TG (~uncertainties in emissions & climate sensitivity): X 3 sources of uncertainty

  7. Reducing an ensemble of scenarios When using the above pattern-scaling approach (GCM-based standardised scenarios are scaled by MAGICC-modelled TGLOB values), we • find a “representative” subset of GCMs, which satisfactorily represents the inter-GCM uncertainty, • choose several TGLOB values, which account for uncertainties in emission scenarios and climate sensitivity.

  8. Choosing a setof TGLOB values TGLOB (modelled by MAGICC for 6 SRES emissions scenarios x 3 climate sensitivities) Considering SRES emissions scenarios and 1.5-4.5K interval for climate sensitivity: 2050:effect of uncertainty in climate sensitivity is (slightly) larger 2100: both effects are about the same CLIMSAVE employs 12 values of TGLOB(~ 4 emissions x 3 climate sensitivity) Reduced set of 3 values: emissions clim.sensitivity high scenario: SRES-A1FI 4.5 K low scenario: SRES-B1 1.5 K middle scen.: SRES-A1b 3.0 K

  9. Defining a representative subset of GCMs • Two approaches are used here to define a representative GCM subset: • A. expert-based judgement “CLIMSAVE” subset • B. applying objective criteria  “EU5a” subset

  10. “CLIMSAVE” subset (method: expert choice) Input: summer (JJA) winter (DJF) ΔTAVG + ΔPREC Output(5 GCMS): MPEH5, HADGEM, GFCM21, NCPCM, MIMR

  11. Defining a “EU5a” subset(based on objective criteria) • Target size of the subset = 5 GCMs • The subsets will consist of: • best GCM [Quality(GCM) ~ ability to reproduce annual cycle of TEMP and PREC in a given 0.5x0.5°gridbox] • central GCM(8D metrics ~ changes in seasonal TEMP and PREC) • +3 most diverse GCMs(maximising a sum of inter-GCM distances; the same metrics) • (prior to analysis, GCM outputs were regridded into 0.5x0.5° grid common with the CRU climatology)

  12. “Best” GCM Best GCM; Q = f [ RV(Temp), RV(Prec)] ...based on RV(Prec) MPEH5 = GCM which is the best in the largest number of gridboxes [Quality(GCM) ~ ability to reproduce annual cycle of TEMP and PREC in a given 0.5x0.5°gridbox] ...based on RV(Temp)

  13. + “Central” GCM ( = closest to Centroid) = GCM which is the Central GCM in the largest number of gridboxes (metrics: Euclidean(8D ~ seasonal changes in TEMP and PREC) • note: MPEH5 and HadGEM, which were found to be among the best GCMs, are also among the three most central GCMs CSMK3

  14. 3 mutually most diverse GCMs HADGEM, GFCM21, IPCM4

  15. 1 centroid 5 GCMs for Europe(3799 0.5°x0.5° land grid boxes) 1 best 3 most diverse 3bests “EU5a”: MPEH5, HADGEM, GFCM21, CSMK3, IPCM4 vs. “CLIMSAVE”: MPEH5, HADGEM, GFCM21, NCPCM, MIMR

  16. GCM subset validation(number of significant differences in AVGs and STDs (subset vs.16 GCMs) EU5a vs. 16GCMs CLIMSAVEvs. 16GCMs • Whole Europe: • - the CLIMSAVE’s problem: significant underestimation of inter-GCM variability in TEMP • - EU5a performs better • both TEMP and PREC • both AVG and STD • UK: • - not such large differences between the two subsets avg(ΔT) std(ΔT) avg(ΔP) insignificant difference: A16G-½S16G, < avgsubset< A16G+½S16G ⅔S16G, < stdsubset< 3/2.S16G std(ΔP)

  17. UKCP09-based climate scenarios • UKCP09 = future climate projection developed by UK Met. Office (http://ukclimateprojections.defra.gov.uk). It is based on: • PPE of HadSM3 simulations (= simplified HadCM3) (PPE = Physically Perturbed Ensemble; 31 key model parameters perturbed) • downscaled by Hadley RCM, • adjustedby outputs from 12 other GCMs, • and disaggregated into 10000 valuesby a statistical emulator • Probabilistic projections of climatic characteristics is given in terms of 10000 possible values (realisations) for each 25x25 km grid box over UK • the projection is available for 3 SRES emission scenarios (low = B1, medium = A1b, high = A1FI) • Aim: Reduce 3 (emissions) x 10,000 realisations to reasonably large ensemble of scenarios (preserving the ensemble variability)

  18. UKCP09 climate scenarios- creating the reduced-form ensemble • 3D space [Tannual, Psummer, Pwinter] • 27 points relate to 3x3x3 combinations of low, med, high changes in the three variables [median, 10th and 90th percentiles along each of 13 lines going through the cube’s center and defined by corners/centres of sides/centres of edges of the cube] • 27 scenarios = the means of 10 neighbours closest to each of 27 points (in a 3D space) Ta Psummer Pwinter 27 climate change scenarios related to 3x3x3 combinations of (low, med, high) changes in dTannual, dPsummer, dPwinter

  19. UKCP09 (2050s): TEMPannual= middle WL-SL WL-SM WL-SH WM-SL WM-SM WM-SH WH-SL WH-SM WH-SH TEMPannual PRECONDJFM PRECAMJJAS

  20. Same but for TEMPannual= low TEMPannual PRECONDJFM PRECAMJJAS slide #20

  21. Same but for TEMPannual = high TEMPannual PRECONDJFM PRECAMJJAS

  22. UKCP09: full vs. reduced ensembles • Q: How does the reduced UKCP09 ensemble represent the original ensemble? • input “full” database = 30000 scenarios = • (3 emission scenarios) x (10000realisations) • for each grid, climate variable and 10 year timeslice) • reduced-form scenarios = 91 scenarios = • (3 emission scenarios) x (27 scenarios representing 3x3x3 combinations of low/medium/high values of Tannual, Psummer, Pwinter • for each grid, climate variable, 2020s and 2050s timeslices • maps: avg(std) from 10000vs. 27scenarios for 2050s (this and following 2 slides) low (SRES-B1) med (SRES-A1b) high (SRES-A1FI) 3 emis.scen. JJA DJF JJA DJF JJA DJF JJA DEC 10000 members 3x 10000 memb. PREC full vs. reduced ensembles: good fitbetween the means 27 clusters 3x 27 clust. JJA DJF JJA DJF JJA DJF JJA DEC

  23. UKCP09: full vs. reduced ensembles 3 emis.scen. low (SRES-B1) med (SRES-A1b) high (SRES-A1FI) JJA DJF JJA DJF JJA DJF JJA DEC 10000 members 3x 10000 memb. TEMP perfect fit 3x 27 clust. 27 clusters 3x 10000 memb. 10000 members PREC perfect fit 3x 27 clust. 27 clusters JJA DJF JJA DJF JJA DJF JJA DEC

  24. UKCP09 vs.GCM (only UK territory) • UKCP09: • original ensemble = 3 emissions x 10000 realisations = 30000 scenarios • reduced ensemble = 3 emissions x 27 scenarios = 81 scenarios • GCMs: • original ensemble = 16 GCMs x 4 emissions x 3clim.sens. = 192 scen. • reduced ensemble = 5 GCMs x 4 emissions x 3clim.sens. = 60 scenarios • UKCP09 vs GCMs: • ........................... UKCP09....... GCMs • full datasets: 30000 vs.192 scenarios • reduced dataset: 81vs.60 scenarios

  25. UKCP09 vs GCMs: avg(PREC) 3 emis.scen. low (SRES-B1) med (SRES-A1b) high (SRES-A1FI) JJA DEC JJA DEC JJA DEC JJA DEC 5GCMs x 3CS reduced dataset GCMs 16GCMs x 3CS full dataset  UKCP09 shows slightly larger reductions in PREC 10000 members UKCP09 27 clusters reduced dataset JJA DEC JJA DEC JJA DEC JJA DEC

  26. UKCP09 vs GCMs: avg(TEMP) 3 emis.scen. low (SRES-B1) med (SRES-A1b) high (SRES-A1FI) JJA DEC JJA DEC JJA DEC JJA DEC 5GCMs x 3CS reduced dataset GCMs 16GCMs x 3CS  significant difference between GCM and UKCP09 full dataset 10000 memb. UKCP09 reduced dataset 27 clusters JJA DEC JJA DEC JJA DEC JJA DEC

  27. UKCP09 vs GCM: std(PREC) 3 emis.scen. low (SRES-B1) med (SRES-A1b) high (SRES-A1FI) JJA DEC JJA DEC JJA DEC JJA DEC 5GCMs x 3CS reduced dataset GCMs  GCMs:the subset reproduces the internal variability 16GCMs x 3CS full dataset • GCMs vs UKCP09:internal UKCP09 ensemble variability is larger (corresponds to larger avg(TAVG) in UKCP scenarios) 10000 members UKCP09  UKCIP09:the reduced-form ensemble reduces internal variability 27 clusters reduced dataset JJA DEC JJA DEC JJA DEC JJA DEC

  28. UKCP09 vs GCMs: std(TEMP) 3 emis.scen. low (SRES-B1) med (SRES-A1b) high (SRES-A1FI) JJA DEC JJA DEC JJA DEC JJA DEC 5GCMs x 3CS reduced dataset GCMs 16GCMs x 3CS  GCMs vs UKCP09:internal UKCP09 ensemble variability is larger full dataset 10000 memb. UKCP09 reduced dataset 27 clusters

  29. Summary + Conclusions (1) • Climate change impact studies require ensembles of climate change scenarios representing known uncertainties. Available scenario datasets were too large for CLIMSAVE, reductions were proposed. • 2 case studies in CLIMSAVE = 2 datasets to reduce in size: • GCMs(CMIP3 dataset of GCMs from various modelling groups): • “large ensemble” = 16 GCMs x 4 emissions x 3 climate sensitivity = 192 scenarios (~ 3 uncertainties) • reduced-form ensemble = 5 GCMs x 4 emissions x 3 climate sensitivity (or 5 GCMs x 3 dTglob) = 60 (15) scenarios • though the “optimum” subset varies across Europe, the single GCM subset still reasonably well represents the inter-GCM variability over majority of European territory • UKCP09[~ PP(HadSM) + HadRM + “statistical emulator”] • large ensemble = 10000 realisations x 3 emission scenarios = 30000 scenarios (structural uncertainties within 10000 members also account for climate sensitivity uncertainty) • reduced-form ensemble = 27 scenarios x 3 emissions = 81 scenarios • within-ensemble variability is lower (effect of natural climate variability is reduced)

  30. Summary + Conclusions (2) • In both ensembles: • the reduced-form scenarios reasonably well represent means and variabilities of the original ensembles • > structural & climate sensitivity & emissions uncertainties are preserved • GCMs vs UKCP09: • except for avg(PREC), significant differences between the 2 ensembles were found • [these differences] >> [the differences related to reducing the original datasets]

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