page 1 ecmwf decadal hindcasts common ensembles experimental set-up stream 2 decadal hindcasts:...
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On representing model uncertainty in climate
predictions
T.N.Palmer
ECMWF
with thanks to Francisco Doblas-Reyes, Thomas Jung and Antje Weisheimer,
ECMWF
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Model uncertainty
Model uncertainty
Scenario uncertainty
Scenario uncertainty
Initial uncertainty
Hawkins and Sutton, 2009
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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
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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
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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
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How can uncertainty be represented in ESMs?
• Multi-model ensembles
• Perturbed parameters
• Stochastic parametrisation
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Seasonal multi-model ensemble
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Seasonal Reforecasts (months 2-4) of El Niño with a comprehensive coupled model
observations
predictions
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Multi-model seasonal reforecasts of El Niño
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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
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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.
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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
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For all their pragmatic value, multi-model ensembles are ad hoc “ensembles of opportunity”.
Component models have common shortcomings, eg due to
limited resolution.
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How can uncertainty be represented in ESMs?
• Multi-model ensembles
• Perturbed parameters
• Stochastic parametrisation
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Deterministic local bulk-formula parametrisation
Increasing scale
;nP X
1X 2X 3X nX... ...
2. pt
u u g u
Vary α
Perturbed Parameters
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How can uncertainty be represented in ESMs?
• Multi-model ensembles
• Perturbed parameters
• Stochastic parametrisation
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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
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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.
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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
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Realisations of stochastic pattern generator
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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.
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“Giorgi” Regions
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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
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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
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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
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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.