Funded under the European Commission Seventh Framework Programme Contract Number: Climate change scenarios incorporated into the CLIMSAVE Integrated Assessment Platform Climate change integrated assessment methodology for cross-sectoral adaptation and vulnerability in Europe For further information contact Martin Dubrovsky ( or visit the project website (

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 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 ( ) coordinated by the Environmental Change Institute, University of Oxford 18 partners from 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 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)

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

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

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  T GLOB values), we – find a “representative” subset of GCMs, which satisfactorily represents the inter-GCM uncertainty, – choose several  T GLOB values, which account for uncertainties in emission scenarios and climate sensitivity.

Choosing a set of  T GLOB values Considering SRES emissions scenarios and K 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  T GLOB (~ 4 emissions x 3 climate sensitivity) Reduced set of 3 values: emissionsclim.sensitivity high scenario:SRES-A1FI4.5 K low scenario:SRES-B11.5 K middle scen.:SRES-A1b3.0 K  T GLOB (modelled by MAGICC for 6 SRES emissions scenarios x 3 climate sensitivities)

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

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

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

+ “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

3 mutually most diverse GCMs HADGEM, GFCM21, IPCM4

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

GCM subset validation (number of significant differences in AVGs and STDs (subset vs. 16 GCMs) avg(ΔT) std(ΔT) avg(ΔP) std(ΔP) CLIMSAVE vs. 16GCMs EU5a vs. 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 insignificant difference: A 16G -½S 16G, < avg subset < A 16G +½S 16G ⅔S 16G, < std subset < 3 /2.S 16G

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 ( 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 values by a statistical emulator Probabilistic projections of climatic characteristics is given in terms of 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 [  T annual,  P summer,  P winter ] 27 points relate to 3x3x3 combinations of low, med, high changes in the three variables [median, 10 th and 90 th 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Ta  P winter  P summer 27 climate change scenarios related to 3x3x3 combinations of (low, med, high) changes in dT annual, dP summer, dP winter

UKCP09 (2050s):  TEMP annual = middle  TEMP annual  PREC ONDJFM  PREC AMJJAS WL-SLWL-SMWL-SHWM-SLWM-SMWM-SHWH-SLWH-SMWH-SH

Same but for  TEMP annual = low  TEMP annual  PREC ONDJFM  PREC AMJJAS slide #20

Same but for  TEMP annual = high  TEMP annual  PREC ONDJFM  PREC AMJJAS

3 emis.scen. high (SRES-A1FI) med (SRES-A1b) low (SRES-B1) UKCP09: full vs. reduced ensembles members 27 clusters  PREC 3x memb. 3x 27 clust. JJADJFJJADJFJJADECJJADJF Q: How does the reduced UKCP09 ensemble represent the original ensemble? input “full” database = scenarios = –( 3 emission scenarios) x ( 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  T annual,  P summer,  P winter for each grid, climate variable, 2020s and 2050s timeslices maps: avg(  std) from vs. 27 scenarios for 2050s (this and following 2 slides) full vs. reduced ensembles: good fit between the means JJADJFJJADJFJJADECJJADJF

3 emis.scen. high (SRES-A1FI) med (SRES-A1b) low (SRES-B1) UKCP09: full vs. reduced ensembles members 27 clusters members 27 clusters  TEMP  PREC 3x memb. 3x 27 clust. 3x memb. 3x 27 clust. JJADJFJJADJFJJADECJJADJF JJADJFJJADJFJJADECJJADJF 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 realisations = 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 UKCP09 vs GCMs: UKCP GCMs full datasets: vs. 192 scenarios reduced dataset: 81 vs. 60 scenarios

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

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

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

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

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 UKCP09 [~ PP(HadSM) + HadRM + “statistical emulator”] – large ensemble = realisations x 3 emission scenarios = scenarios (structural uncertainties within 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]