Preclim part 1 declips model weighting nccr wp2 meeting 5 october 2010
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PRECLIM (part 1) DeClips / model weighting NCCR WP2 Meeting, 5 October 2010. Andreas Weigel, Reidun Gangsto, Andreas Fischer, Reto Knutti, Mark Lingier, Christof Appenzeller. decadal. NCCR 1+2. Predictions at different time-scales. monthly. seasonal. weather. multi-decadal. Declips

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PRECLIM (part 1) DeClips / model weighting NCCR WP2 Meeting, 5 October 2010

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Preclim part 1 declips model weighting nccr wp2 meeting 5 october 2010

PRECLIM (part 1)DeClips / model weightingNCCR WP2 Meeting, 5 October 2010

Andreas Weigel, Reidun Gangsto, Andreas Fischer,

Reto Knutti, Mark Lingier, Christof Appenzeller


Predictions at different time scales

decadal

NCCR 1+2

Predictions at different time-scales

monthly

seasonal

weather

multi-decadal

Declips

(with SwissRe)

Preclim

Traditional

MCH

NCCR 3


Sources of uncertainty in decadal mean temperature projections

Sources of uncertainty in decadal mean temperature projections

Hawkins and Sutton, 2009


Decadal predictions

Decadal predictions

  • Decadal time scale represents planning horizon for

    • infrastructure

    • energy

    • insurance

    • agriculture

    • fishery

  • Pioneering studies indicate

    potential decadal predictabi-

    lity in North Atlantic region (Smith et al. 2008, Keenlyside et al. 2008)

  • Currently field of intensive research. Will be considered in IPCC AR5.

  • Experimental data-set available from EU FP6 ENSEMBLES

Keenlyside et al (2008)


Ensembles decadal predictions

ENSEMBLES decadal predictions

Ensemble size

Hindcast-Periods

Model

3

1960-1970

3

1965-1975

IFS / HOPE (ECMWF)

3

3

2005-2015

3

1960-1970

3

1965-1975

Different initial con-ditions

HadGEM2 (Met Office)

3

2005-2015

3

3

1960-1970

3

1965-1975

ARPEGE4 (CERFACS)

3

2005-2015

3

3

1960-1970

3

1965-1975

ECHAM5 (IfM Kiel)

3

3

2005-2015

9

1960-1970

Per-turbed physics

9

1961-1971

DePreSys (Met Office)

9

1962-1972

9

9

2005-2015


Research tasks

Research tasks

  • Establish verification framework for decadal forecasts

    • Small sample size

    • Lack of observations for 3D ocean in earlier decades

    • Two-step assessment: Trend & remaining fluctuations

  • Skill assessment for Europe (T and other variables)

    • Comparison to other strategies (stat. models, persistence, …)

    • Added value

  • Investigate potential applications for the reinsurance business (SwissRe application model and/or toymodel)

  • Apply to newer model runs according to availability (e.g. CMIP5)


Hindcasts of t2 c

Hindcasts of T2 (°C)

Global annual average T2 (IFS vs observations)

Anomalies (°C)

Global annual average T2 (ARPEGE vs observations)

Need to correct for drift and systematic errors


Predictions at different time scales1

decadal

NCCR 1+2

Predictions at different time-scales

monthly

seasonal

weather

multi-decadal

Declips

(with SwissRe)

Preclim

Traditional

MCH

NCCR 3


Probabilistic climate projections

PDF

Probabilistic climate projections

Modelled Climate

Change Signals

Weighting?

Statistical model

(e.g. Buser et al. 2009)

(presentation A. Fischer)


First preclim paper

First PRECLIM-Paper…

Weigel et al., J. Clim. 2010


Effects of weighting

Effects of weighting

Increase of

error (MSE)

Benchmark

Equal weights

Decrease of

error (MSE)

Both models have same skill

Model 2 inifintely better than Model 1

Weigel et al, 2010, J. Clim.


Effects of weighting1

Effects of weighting

Increase of

error (MSE)

Benchmark

Equal weights

Optimal weights

Decrease of

error (MSE)

Both models have same skill

Model 2 inifintely better than Model 1

Weigel et al, 2010, J. Clim.


Effects of weighting2

Effects of weighting

Increase of

error (MSE)

Worst possible weights

Benchmark

Equal weights

Optimal weights

Decrease of

error (MSE)

Both models have same skill

Model 2 inifintely better than Model 1

Weigel et al, 2010, J. Clim.


Effects of weighting3

Effects of weighting

Increase of

error (MSE)

Random weights

Benchmark

Equal weights

Optimal weights

Decrease of

error (MSE)

Both models have same skill

Model 2 inifintely better than Model 1

Weigel et al, 2010, J. Clim.


Probabilistic climate projections1

PDF

Probabilistic climate projections

Modelled Climate

Change Signals

Weighting?

Statistical model

(e.g. Buser et al. 2009)

(presentation A. Fischer)


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