Transcript Climate change integrated assessment methodology for cross

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