Federal Department of Home Affairs FDHAFederal Office of Meteorology and Climatology MeteoSwiss
Climate Change Projections for Switzerland: A Bayesian Multi-Model Combination using ENSEMBLES Regional Climate Models
11th International Meeting on Statistical Climatology, 12 July 2010, Edinburgh
Andreas Fischer, Andreas Weigel, Mark Liniger, Christoph Buser, Christof Appenzeller
2 Climate Services, IMSC Edinburgh | 12 July [email protected]
ENSEMBLES R2TB
AOGCMs RCMs@25km
HadCM3
HIRHAM (Met.No)
REMO (MPI)
SRES A1B
ECHAM5
Low sens.
High sens.
Standard sens.
ARPEGE
CGCM3
BCMRCA (SMHI)
HadRM3 (Met Office)RCA (SMHI)
HadRM3 (Met Office)RCA3 (C4I)
CLM (ETHZ)PROMES (UCLM)
HIRHAM (DMI)RACMO (KNMI)
RCA (SMHI)
ALADIN v1 (CNRM)
HIRHAM (DMI)
REGCM3(ICTP)
CRCM (OURANOS)
RRCM (VMGO)
IPSL CLM (GKSS)
HadRM3 (Met Office)
ALADIN v2 (CNRM)
HIRHAM (Met.No)
HIRHAM (DMI) Final Report (2009)
RCMs@25kmAOGCMs
1950 - 2050
8 AOGCMs / 21 Model Chains 6 AOGCMs / 15 Model Chains
2050 - 2100
HadCM3
REMO (MPI)
ECHAM5
Low sens.
High sens.
Standard sens.
ARPEGE
BCMRCA (SMHI)
HadRM3 (Met Office)
RCA (SMHI)
HadRM3 (Met Office)
RCA3 (C4I)
CLM (ETHZ)
HIRHAM (DMI)
RACMO (KNMI)
RCA (SMHI)
HIRHAM (DMI)
REGCM3(ICTP)
HadRM3 (Met Office)
ALADIN v2 (CNRM)
HIRHAM (DMI)
3 Climate Services, IMSC Edinburgh | 12 July [email protected]
Derivation of Probablistic Scenarios
Modelled Climate Change Signals
?Bayes Algorithm(Buser et al., 2009)
Assumptions transparent
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Bayesian Multi-Model Combination (Buser et al., 2009)
Obs NOW
Models NOW
Models FUTURE
„Obs“ FUTURE
Seasonally averaged 30yr time periods
5 Climate Services, IMSC Edinburgh | 12 July [email protected]
Bayesian Multi-Model Combination (Buser et al., 2009)
Obs NOW
Models NOW
Models FUTURE
„Obs“ FUTURE
Mean Climate Shift Model Projection Errors
NOW FUTURE
6 Climate Services, IMSC Edinburgh | 12 July [email protected]
Bayesian Multi-Model Combination (Buser et al., 2009)
Obs NOW
Models NOW
Models FUTURE
„Obs“ FUTURE
Mean Climate Shift Model Projection Errors
NOW FUTURE
• μ and βi non identifiable
• Assumption has to be taken about projection error Δβi ~ N(0; σ2
β)
7 Climate Services, IMSC Edinburgh | 12 July [email protected]
Bayesian Multi-Model Combination (Buser et al., 2009)
Prior p(x)
Posterior p(x|data)
Obs NOW
Models NOW
Models FUTURE
„Obs“ FUTURE
Likelihood p(data|x)
P(x|data) p(x) * p(data|x)
Gibbs Sampler
8 Climate Services, IMSC Edinburgh | 12 July [email protected]
Sensitivity Experiments: Effect of Likelihood
Climate ChangeSignal
Likelihood affects variance and location of posterior distribution
All prior distributions set to be uninformative
9 Climate Services, IMSC Edinburgh | 12 July [email protected]
Bayesian Multi-Model Combination (Buser et al., 2009)
Prior p(x)
Posterior p(x|data)
Obs NOW
Models NOW
Models FUTURE
„Obs“ FUTURE
Likelihood p(data|x)
P(x|data) p(x) * p(data|x)
Gibbs Sampler
10 Climate Services, IMSC Edinburgh | 12 July [email protected]
Sensitivity Experiments: Effect of Prior
Projection Uncertainty
Mea
n C
lim
ate
Sh
ift
The uncertainty in Δμ is increased with a wider prior-setting for Δβi
11 Climate Services, IMSC Edinburgh | 12 July [email protected]
CC Signal
Sensitivity Experiments: Effect of Prior
CC Signal
Outlier
Informative Prior Δβi
Non-Informative Prior Δβi
Central tendency of posterior distributions also affected by prior
CC Signal
12 Climate Services, IMSC Edinburgh | 12 July [email protected]
Application of Algorithm using ENSEMBLES data
1. Estimation of Projection Uncertainty (σ2β)
2. Role of Internal Variability
3. Independent Model Data
Different considerations:
13 Climate Services, IMSC Edinburgh | 12 July [email protected]
1. Estimating Projection Uncertainty
Assumption: Projection Uncertainty is fully sampled by range of available model simulations
ECHAM
HadCM3Q0
(2) RCM Uncertainty
8 different GCMs
(1) GCM Uncertainty
Smoothing of timeseries by polynomial fit (Hawkins & Sutton, 2009)
14 Climate Services, IMSC Edinburgh | 12 July [email protected]
2. Internal Variability
(1) As a pre-processing step we remove internal variability from time-series
(2) Calculate posterior distributions with Bayes Algorithm
(3) Add internal variability to posterior distribution of μ
15 Climate Services, IMSC Edinburgh | 12 July [email protected]
30-yr Running Mean
4th order polynomial fit
(Hawkins and Sutton, 2009)
Summer Temperature over CHNE (Model: ETHZ – HadCM3Q0)
2. Internal Variability
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30-yr Running Mean
4th order polynomial fit
(Hawkins and Sutton, 2009)
Summer Temperature over CHNE (Model: ETHZ – HadCM3Q0)
2. Internal Variability
17 Climate Services, IMSC Edinburgh | 12 July [email protected]
ECHAM
HadCM3Q0
3. Independent Model Data
ECHAM HadQ0 HadQ3 HQ16 ARP. BCM
ECHAM
HadQ0
HadQ3
HQ16
ARP.
BCM
DJF Temperature 1980-2009 (AL)
Average all RCMs driven by the same GCM
18 Climate Services, IMSC Edinburgh | 12 July [email protected]
Probabilistic Climate Change Scenarios
Orography of Switzerland
Reference Period 1980 - 2009
Northeastern Switzerland
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Swiss Climate Scenario (A1B)
GCM groups
203520602084
GCM-RCMchains
Temperature (K)
Internal Variability
20 Climate Services, IMSC Edinburgh | 12 July [email protected]
Swiss Climate Scenario (A1B)
Relative Precipitation
GCM groups
GCM-RCMchains
203520602084
Internal Variability
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Conclusions
The Bayes Algorithm by Buser et al. (2009) is a transparent tool for generating probabilistic climate change scenarios.
The uncertainty range in the climate change signal is highly dependent on the prior-settings of the projection uncertainty.
The Buser Algorithm does not account for internal variability. To circumvent this problem a pragmatic solution has been proposed.
The probabilistic climate change scenarios for Northeastern Switzerland show a continous increase in temperature over the 21st century. For precipitation only in summer a signal in the second half of the century is detectable.
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Swiss Climate Scenarios: Precipitation
DJF Precipitation Change [%]
2035 2060 2084
JJA Precipitation Change [%]
2035 2060 2084
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Effect correlated models
Delta Mu JJA T2 CHN
KNMI-ECHAM / ETHZ-HadQ0 / SMHI-HadQ3 / C4I-HadQ16 / CNRM-ARPEGE / SMHI-BCM / OURANOS
ECHAM av. / HadQ0 av. / HadQ3 av. / HadQ16 av. / CNRM-ARPEGE / BCM av. / OURANOS
Average of 1 GCM group / Rest as standard
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Climate Scenarios
Global Mean Temperature wrt 1980-2009
B1
A1BA2
2035 2060 2084
comm
?
[K]
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Pattern Scaling with CMIP A1B
Bayes Estimate 2035
Scaled from 2060
Scaled from 2084Temperature Relative Precipitation
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Aim: Update of Probabilistic Scenarios
OcCC (2007)203020502070
Relative PrecipitationTemperature
Probabilistic Scenarios for Northern and Southern Switzerland based on PRUDENCE RCM simulations
28 Climate Services, IMSC Edinburgh | 12 July [email protected]
Model validation
CHS
CHWCHNE
Orography
Temperature (°C)
Temperature (°C)
Precipiation (mm/mt)
EOBS v3
EOBS v3
EOBS v3
(1980 – 2009)