On representing model uncertainty in climate predictions

On representing model uncertainty in climate

predictions

T.N.Palmer

ECMWF

with thanks to Francisco Doblas-Reyes, Thomas Jung and Antje Weisheimer,

ECMWF

Model uncertainty

Model uncertainty

Scenario uncertainty

Scenario uncertainty

Initial uncertainty

Hawkins and Sutton, 2009

Standard Numerical Ansatz for Climate Model

Deterministic local bulk-formula parametrisation

Increasing scale

;nP X

Eg momentum“transport” by:

•Turbulent eddies in boundary layer

•Orographic gravity wave drag.

•Convective clouds

1X 2X 3X nX... ...

2. pt

u u g uEg

Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere

Land surfaceLand surfaceLand surfaceLand surfaceLand surface

Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice

Sulphateaerosol

Sulphateaerosol

Sulphateaerosol

Non-sulphateaerosol

Non-sulphateaerosol

Carbon cycle Carbon cycle

Atmosphericchemistry

Ocean & sea-icemodel

Sulphurcycle model

Non-sulphateaerosols

Carboncycle model

Land carboncycle model

Ocean carboncycle model

Atmosphericchemistry

Atmosphericchemistry

Off-linemodeldevelopment

Strengthening coloursdenote improvementsin models

1975 1985 1992 1997

The

Met

.Offi

ce H

adle

y C

entr

e

Towards Comprehensive Earth System Models

1970 2000

Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere

Land surfaceLand surfaceLand surfaceLand surfaceLand surface

Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice

Sulphateaerosol

Sulphateaerosol

Sulphateaerosol

Non-sulphateaerosol

Non-sulphateaerosol

Carbon cycle Carbon cycle

Atmosphericchemistry

Ocean & sea-icemodel

Sulphurcycle model

Non-sulphateaerosols

Carboncycle model

Land carboncycle model

Ocean carboncycle model

Atmosphericchemistry

Atmosphericchemistry

Off-linemodeldevelopment

Strengthening coloursdenote improvementsin models

1975 1985 1992 1997

The

Met

.Offi

ce H

adle

y C

entr

e

1970 2000

Uncertainty

A Missing Box

How can uncertainty be represented in ESMs?

• Multi-model ensembles

• Perturbed parameters

• Stochastic parametrisation

Seasonal multi-model ensemble

Seasonal Reforecasts (months 2-4) of El Niño with a comprehensive coupled model

observations

predictions

Multi-model seasonal reforecasts of El Niño

precipitation in DJFstart dates: Nov hindcast period: 1991-2005

lower tercile

Amazon Central America Northern Europe

Multi-model Seasonal Forecast Reliability

Failure of multi-model ensemlble

Slide 13

Surface Pressure

Potential Vorticity on 315K

Blocking Anticyclone

As recognised in AR4, the current

generation of climate models has difficulty simulating a number of internal

modes of climate variability such as

the persistent blocking

anticyclone.

Blocking Index. DJFM 1960-2003

ERA-40

T159

T1259

T1259 run on NSF Cray XT4 “Athena” (two months of dedicated usage) Similar results found by M.Matsueda MRI Japan

For all their pragmatic value, multi-model ensembles are ad hoc “ensembles of opportunity”.

Component models have common shortcomings, eg due to

limited resolution.

How can uncertainty be represented in ESMs?

• Multi-model ensembles

• Perturbed parameters

• Stochastic parametrisation

Deterministic local bulk-formula parametrisation

Increasing scale

;nP X

1X 2X 3X nX... ...

2. pt

u u g u

Vary α

Perturbed Parameters

How can uncertainty be represented in ESMs?

• Multi-model ensembles

• Perturbed parameters

• Stochastic parametrisation

A stochastic-dynamic paradigm for the Earth-System model

Computationally-cheap nonlinear stochastic-dynamic models, providing specific possible realisations of sub-grid motions rather than sub-grid bulk effects

Coupled over a range of scales

Increasing scale

ECMWF Tech Memo 598

SAC 2009

Spectral Stochastic Backscatter Scheme

• Origins: Leith (1990), Mason and Thomson (1992)

• Shutts, G.J. (2005). A kinetic energy backscatter algorithm for use in ensemble prediction systems. Q.J.R.Meteorol.Soc. 131, 3079

• Berner, J. et al (2009). A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos.Sci., 66, 603-626.

SAC2009

Slide 21

Backscatter Algorithm

Streamfunction forcing Pattern using spectral AR(1) processes as SPPT

Dtot is a smoothed total dissipation rate, normalized here by

Btot and bR is the backscatter ratio

Realisations of stochastic pattern generator

In ENSEMBLES we have tested the relative ability of these different representations of uncertainty:

Multi-model ensemblesPerturbed parametersStochastic physics

to make skilful probabilistic seasonal climate predictions.

“Giorgi” Regions

dry wet dry wetAustralia

1 2 2 3MM best

Amazon Basin3 0 1 3

PP bestSouthern South America

1 1 1 1SP best

Central America2 3 3 2

Western North America3 3 3 3

Central North America1 1 3 3

Eastern North America1 1 2 3

Alaska3 1 2 3

Greenland1 3 2 3

Mediterranean3 3 3 3

Northern Europe2 2 3 3

Western Africa3 1 2 3

Eastern Africa3 3 2 2

Southern Africa3 3 3 2

Sahel1 3 2 1

South East Asia1 1 1 0

East Asia3 3 3 3

South Asia3 3 3 3

Central Asia2 3 2 2

Tibet1 1 1 2

North Asia3 2 1 1

precipitationJJA DJF

1991-2005lead times: 2-4 monthsDry=lower tercile

Wet=upper tercile Which is best?Brier Skill Score

cold warm cold warmAustralia

3 3 3 1MM best

Amazon Basin3 1 1 1

PP bestSouthern South America

1 1 3 2SP best

Central America3 1 3 1

Western North America1 1 3 1

Central North America1 1 2 2

Eastern North America3 3 2 3

Alaska3 3 2 2

Greenland1 1 2 2

Mediterranean3 2 1 3

Northern Europe2 2 3 3

Western Africa1 1 3 3

Eastern Africa1 1 2 3

Southern Africa1 2 1 1

Sahel1 2 1 3

South East Asia1 1 2 1

East Asia3 2 1 2

South Asia3 1 3 3

Central Asia1 2 3 2

Tibet3 1 3 3

North Asia1 2 1 2

temperatureJJA DJF

1991-2005lead times: 2-4 months

Brier Skill Score

Cold=lower tercile

Warm=upper tercile

precipitation over Northern Europe land (north of 48ºN) in DJFstart dates: Nov 1st. hindcast period: 1991-2005

lower tercile

stochastic physics #7

BSS(∞)=0.087BSS(∞)=-0.018

perturbed physicsmulti-model

BSS(∞)=-0.031

Multi-model Seasonal Forecast Reliability

Conclusions

• Stochastic parametrisation and perturbed parameter methodologies are competitive with the traditional multi-model approach to representing model uncertainty

• Stochastic parametrisation “wins” overall for atmospheric variables, but needs to be extended to the ocean and the land surface.

• The ECMWF THOR integrations will be started next year using the latest stochastic parametrisation schemes.