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

Funded under the European CommissionSeventh Framework Programme

Contract Number: 244031

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 (email: [email protected]) or visit the project website (www.climsave.eu)

Climate Change Integrated Methodology for Cross-Sectoral Adaptation and Vulnerability in Europe

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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


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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)

– 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, and2. it satisfactorily represents known uncertainties in future climate

projections.

http://www.climsave.eu/


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GCM-based scenarios

(based on monthly GCM outputsfrom 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 ΔTG 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) = ΔXS x ΔTG(t) ΔTG = change in global mean temperature

ΔXS = standardised scenario (related to ΔTG = 1K; derived from GCMs)

uncertainty in TG

(~uncertainties in emissions& climate sensitivity):


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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.

Choosing a setof TGLOB values

Considering SRES emissions scenarios and 1.5-4.5K interval for climate sensitivity:2050: effect of uncertainty in climate sensitivity is (slightly) larger2100: both effects are about the same

CLIMSAVE employs 12 values of TGLOB (~ 4 emissions x 3 climate sensitivity)

Reduced set of 3 values: emissionsclim.sensitivity

high scenario: SRES-A1FI 4.5 Klow scenario: SRES-B1 1.5 Kmiddle scen.: SRES-A1b 3.0 K

TGLOB

(modelled by MAGICC for 6 SRES emissions scenarios x 3 climate sensitivities)


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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)

ΔT

AV

GΔ

PR

EC

Output (5 GCMS): MPEH5, HADGEM, GFCM21, NCPCM, MIMR

+

Input:


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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(3799 0.5°x0.5° land grid boxes)

“EU5a”: MPEH5, HADGEM, GFCM21, CSMK3, IPCM4 vs.

“CLIMSAVE”: MPEH5, HADGEM, GFCM21, NCPCM, MIMR

3 mostdiverse

1 centroid

1 best

GCM subset validation(number of significant differences in AVGs and STDs (subset vs. 16 GCMs)

avg(

ΔT

)st

d(Δ

T)

avg(

ΔP

)st

d(Δ

P)

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:A16G-½S16G, < avgsubset < A16G+½S16G

⅔S16G, < stdsubset < 3/2.S16G


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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

• 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)

http://ukclimateprojections.defra.gov.uk/

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

Pwinter

Psummer

27 climate change scenarios related to 3x3x3 combinations of (low, med, high) changes in dTannual, dPsummer, dPwinter

UKCP09 (2050s): TEMPannual = middleT

EM

Pan

nual

PR

EC

ON

DJF

MP

RE

CA

MJJ

AS

WL-SL WL-SM WL-SH WM-SL WM-SM WM-SH WH-SL WH-SM WH-SH

Same but for TEMPannual = low T

EM

Pan

nual

PR

EC

ON

DJF

MP

RE

CA

MJJ

AS

slide #20

Same but for TEMPannual = high T

EM

Pan

nual

PR

EC

ON

DJF

MP

RE

CA

MJJ

AS

3 emis.scen.high (SRES-A1FI)med (SRES-A1b)low (SRES-B1)

UKCP09: full vs. reduced ensembles10

000

mem

bers

27 c

lust

ers

PR

EC

3x

10

00

0 m

em

b.

3x 2

7 cl

ust.

JJA DJF JJA DJF JJA DECJJA DJF

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

• maps: avg(std) from 10000 vs. 27 scenarios for 2050s (this and following 2 slides)

full vs. reduced ensembles: good fit between the means



UKCP09: full vs. reduced ensembles10

000

mem

bers

27 c

lust

ers

1000

0 m

embe

rs27

clu

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s

TE

MP

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3x

10

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0 m

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3x 2

7 cl

ust.

3x

10

00

0 m

em

b.

3x 2

7 cl

ust.



perfect fit

perfect fit


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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

• UKCP09 vs GCMs: ........................... UKCP09....... GCMs

full datasets: 30000 vs. 192 scenariosreduced dataset: 81 vs. 60 scenarios


UKCP09 vs GCMs: avg(PREC)

JJA DEC JJA DEC JJA DECJJA DEC

1000

0 m

embe

rs27

clu

ster

s16

GCM

s x

3CS

5GCM

s x

3CS

UKC

P09

GCM

s


full

da

tase

t

UKCP09 shows slightly larger reductions in PREC

redu

ced

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ase

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duce

d d

ata

set


UKCP09 vs GCMs: avg(TEMP)27

clu

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UKC

P09

GCM

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full

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5GCM

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3CS

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16G

CMs

x 3C

S

significant difference between GCM and UKCP09


UKCP09 vs GCM: std(PREC)


1000

0 m

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s16

GCM

s x

3CS

5GCM

s x

3CS

UKC

P09

GCM

s

full

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tase

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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

redu

ced

dat

ase

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duce

d d

ata

set


UKCP09 vs GCMs: std(TEMP)27

clu

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UKC

P09

GCM

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full

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tase

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1000

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emb.

5GCM

s x

3CS

redu

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d d

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set

16G

CMs

x 3C

S

JJA JJA JJAJJADEC DEC DEC DEC

GCMs vs UKCP09: internal UKCP09 ensemble variability is larger


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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)


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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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