11 th international meeting on statistical climatology, 12 july 2010, edinburgh

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)


Derivation of Probablistic Scenarios

Modelled Climate Change Signals

PDF

?Bayes Algorithm(Buser et al., 2009)

Assumptions transparent


Bayesian Multi-Model Combination (Buser et al., 2009)

Obs NOW

Models NOW

Models FUTURE

„Obs“ FUTURE

Seasonally averaged 30yr time periods



Obs NOW

Models NOW

Models FUTURE

„Obs“ FUTURE

Mean Climate Shift Model Projection Errors

NOW FUTURE



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)



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


Sensitivity Experiments: Effect of Likelihood

Climate ChangeSignal

Likelihood affects variance and location of posterior distribution

All prior distributions set to be uninformative



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


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


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


Application of Algorithm using ENSEMBLES data

1. Estimation of Projection Uncertainty (σ2β)

2. Role of Internal Variability

3. Independent Model Data

Different considerations:


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)


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


30-yr Running Mean

4th order polynomial fit

(Hawkins and Sutton, 2009)

Summer Temperature over CHNE (Model: ETHZ – HadCM3Q0)



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


Probabilistic Climate Change Scenarios

Orography of Switzerland

Reference Period 1980 - 2009

Northeastern Switzerland


Swiss Climate Scenario (A1B)

GCM groups

203520602084

GCM-RCMchains

Temperature (K)

Internal Variability


Swiss Climate Scenario (A1B)

Relative Precipitation

GCM groups

GCM-RCMchains

203520602084

Internal Variability


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.


Swiss Climate Scenarios: Precipitation

DJF Precipitation Change [%]

2035 2060 2084

JJA Precipitation Change [%]

2035 2060 2084


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


Climate Scenarios

Global Mean Temperature wrt 1980-2009

B1

A1BA2

2035 2060 2084

comm

?

[K]


Pattern Scaling with CMIP A1B

Bayes Estimate 2035

Scaled from 2060

Scaled from 2084Temperature Relative Precipitation


Swiss Climate Scenarios

A2B1

A1B


Aim: Update of Probabilistic Scenarios

OcCC (2007)203020502070

Relative PrecipitationTemperature

Probabilistic Scenarios for Northern and Southern Switzerland based on PRUDENCE RCM simulations


Model validation

CHS

CHWCHNE

Orography

Temperature (°C)

Temperature (°C)

Precipiation (mm/mt)

EOBS v3

EOBS v3

EOBS v3

(1980 – 2009)

11 th international meeting on statistical climatology, 12 july 2010, edinburgh

Documents