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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: [email protected]) or visit the project website (www.climsave.eu) Funded under the European Commission Seventh Framework Programme Contract Number: 244031 Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe 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 UKCP09 data for Scottish case study + representativeness of the reduced-form ensembles www.CLIMSAVE.eu 3. Comparison of GCM-based vs. UKCP09 scenarios 4. Summary & Conclusion Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe Introduction – CLIMSAVE project CLIMSAVE project (www.climsave.eu; 2010-2013) • coordinated by the Environmental Change Institute, University of Oxford • 18 partners from 13 countries (incl. China and Australia) www.CLIMSAVE.eu – 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 data representing present and future climates – When creating an ensemble of climate change scenarios for 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. Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe GCM-based scenarios (based on monthly GCM outputs from IPCC-AR4 database /~CMIP3/; Europe) www.CLIMSAVE.eu GCMs in CMIP3 database We use 16 SRES-A2 simulations of 24 GCMs x 6 emission scenarios (incomplete matrix). Pattern scaling is used to create a set of climate change scenarios Pattern scaling approach allows to reflect multiple uncertainties: ΔX(t) = ΔXS x ΔTG(t) ΔTG = change in global mean temperature ΔXS = standardised scenario (related to ΔTG = 1K; derived from GCMs) - where several ΔTG values are used to multiply several GCM-based patterns uncertainty in TG (~uncertainties in emissions & climate sensitivity): uncertainty in pattern (~ modelling uncertainty): X 3 sources of uncertainty Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe 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. www.CLIMSAVE.eu Choosing a set of 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: high scenario: low scenario: middle scen.: emissions SRES-A1FI SRES-B1 SRES-A1b clim.sensitivity 4.5 K 1.5 K 3.0 K Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe 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 www.CLIMSAVE.eu “CLIMSAVE” subset (method: expert choice) Input: ΔPREC + winter (DJF) ΔTAVG summer (JJA) Output (5 GCMS): MPEH5, HADGEM, GFCM21, NCPCM, MIMR Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe Defining a “EU5a” subset (based on objective criteria) • Target size of the subset = 5 GCMs • The subsets will consist of: o best GCM [Quality(GCM) ~ ability to reproduce annual cycle of TEMP and PREC in a given 0.5x0.5° gridbox] o central GCM (8D metrics ~ changes in seasonal TEMP and PREC) www.CLIMSAVE.eu o +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) “Best” GCM ...based on RV(Prec) Best GCM; Q = f [ RV(Temp), 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) + “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) CSMK3 • note: MPEH5 and HadGEM, which were found to be among the best GCMs, are also among the three most central GCMs 3 mutually most diverse GCMs HADGEM, GFCM21, IPCM4 5 GCMs for Europe 1 centroid (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 GCM subset validation (number of significant differences in AVGs and STDs (subset vs. 16 GCMs) avg(ΔT) CLIMSAVE vs. 16GCMs EU5a vs. 16GCMs • Whole Europe: - the CLIMSAVE’s problem: significant underestimation of inter-GCM variability in TEMP std(ΔP) avg(ΔP) std(ΔT) - EU5a performs better • both TEMP and PREC • both AVG and STD • UK: - not such large differences between the two subsets insignificant difference: A16G-½S16G, < avgsubset < A16G+½S16G ⅔S16G, < stdsubset < 3/2.S16G Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe 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, – adjusted by outputs from 12 other GCMs, – and disaggregated into 10000 values by a statistical emulator www.CLIMSAVE.eu • 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) 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 UKCP09 (2050s): TEMPannual = middle PRECAMJJAS PRECONDJFM TEMPannual WL-SL WL-SM WL-SH WM-SL WM-SM WM-SH WH-SL WH-SM WH-SH PRECAMJJAS PRECONDJFM TEMPannual Same but for TEMPannual = low slide #20 PRECAMJJAS PRECONDJFM TEMPannual Same but for TEMPannual = high 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 (10000 realisations) • 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 low (SRES-B1) JJA DJF med (SRES-A1b) high (SRES-A1FI) 3 emis.scen. JJA DJF JJA DJF JJA DEC JJA DEC 3x 10000 memb. 10000 members • maps: avg(std) from 10000 vs. 27 scenarios for 2050s (this and following 2 slides) 3x 27 clust. 27 clusters PREC full vs. reduced ensembles: good fit between the means JJA DJF JJA DJF JJA DJF low (SRES-B1) JJA DJF med (SRES-A1b) high (SRES-A1FI) 3 emis.scen. JJA DJF JJA DJF JJA DEC 3x 10000 memb. 10000 members UKCP09: full vs. reduced ensembles 27 clusters PREC perfect fit 3x 27 clust. 3x 10000 memb. 3x 27 clust. 27 clusters 10000 members TEMP perfect fit Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe 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 3 clim.sens. = 192 scen. – reduced ensemble = 5 GCMs x 4 emissions x 3 clim.sens. = 60 scenarios www.CLIMSAVE.eu • UKCP09 vs GCMs: ........................... UKCP09....... GCMs full datasets: 30000 vs. 192 reduced dataset: 81 vs. 60 scenarios scenarios UKCP09 vs GCMs: avg(PREC) DEC JJA DEC JJA DEC JJA DEC full dataset UKCP09 shows slightly larger reductions in PREC JJA DEC JJA DEC JJA DEC reduced dataset JJA reduced dataset DEC 16GCMs x 3CS JJA med (SRES-A1b) high (SRES-A1FI) 3 emis.scen. 27 clusters UKCP09 10000 members GCMs 5GCMs x 3CS low (SRES-B1) UKCP09 vs GCMs: avg(TEMP) JJA DEC JJA DEC JJA DEC JJA DEC reduced dataset DEC between GCM and UKCP09 27 clusters UKCP09 10000 memb. significant difference full dataset 16GCMs x 3CS GCMs 5GCMs x 3CS JJA med (SRES-A1b) high (SRES-A1FI) 3 emis.scen. JJA DEC JJA DEC JJA DEC reduced dataset low (SRES-B1) UKCP09 vs GCM: std(PREC) DEC JJA DEC JJA DEC GCMs: the subset reproduces the internal variability GCMs vs UKCP09: internal UKCP09 ensemble variability is larger (corresponds to larger avg(TAVG) in UKCP scenarios) UKCIP09: the reduced-form ensemble reduces internal variability JJA DEC JJA DEC JJA DEC JJA DEC reduced dataset JJA full dataset DEC reduced dataset 16GCMs x 3CS JJA med (SRES-A1b) high (SRES-A1FI) 3 emis.scen. 27 clusters UKCP09 10000 members GCMs 5GCMs x 3CS low (SRES-B1) UKCP09 vs GCMs: std(TEMP) DEC JJA DEC JJA DEC 16GCMs x 3CS GCMs 27 clusters UKCP09 10000 memb. GCMs vs UKCP09: internal UKCP09 ensemble variability is larger reduced dataset JJA full dataset DEC 5GCMs x 3CS JJA med (SRES-A1b) high (SRES-A1FI) 3 emis.scen. reduced dataset low (SRES-B1) Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe 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 www.CLIMSAVE.eu • 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) Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe 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] www.CLIMSAVE.eu