systematic stochastic modeling of atmospheric …38 summary normal form for reduced stochastic...
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Systematic stochastic modeling
of atmospheric variability
Christian FranzkeMeteorological Institute
Center for Earth System Research and Sustainability
University of Hamburg
Daniel Peavoy and Gareth Roberts (Warwick)
Daan Crommelin (CWI) and Andy Majda (NYU)
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Outline
1) Motivation
2) Stochastic Mode Reduction
3) Physical Constraints
4) Bayesian Parameter Estimation
5) Results
6) Summary
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Scales
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LongRange Forecasts
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Reduced Stochastic Climate Models
→ Computationally much cheaper
→ Capture essential dynamics
Improved extended range forecasting Large ensemble forecasting Longterm climate studies (e.g. paleoclimate) Long control simulations to estimate extremes Extreme Event Prediction
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Reduced Order Models
Slow Climate Modes
Fast Weather Modes
Reduced Model
Assumption: Time scale separation
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Reduced Order Models
Assumption: Time scale separation
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Stochastic Modeling
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Outline
1) Motivation
2) Stochastic Mode Reduction
3) Physical Constraints
4) Bayesian Parameter Estimation
5) Results
6) Summary
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Stochastic Mode Reduction
Equations of motions Energy conservation
Decompose u into (x,y)
x: slow compomenty: fast component
dudt
=F+Lu+ I (u ,u) u∗I (u ,u)=0
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Stochastic Mode Reduction
Fast nonlinear interactions: I(y,y)
e.g. OhrnsteinUhlenbeck Process → Continous time version of AR(1)
Majda et al.1999, 2001, 2008; Franzke et al. 2005; Franzke and Majda 2006
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Stochastic Mode Reduction
Equations of motions Energy conservation
dudt
=F+Lu+ I (u ,u) u∗I (u ,u)=0
dx=(~F+
~L x+~I (x , x)+M (x , x , x))dt+σA dW A+σM (x)dW M
Reduced Model:
Mode Reduction
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Stochastic Mode Reduction
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Stochastic Mode Reduction
Solve for y:
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Stochastic Mode Reduction
For
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Stochastic Mode Reduction
Plug into equation for x:
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Stochastic Mode Reduction
Plug into equation for x:
CAM Noise
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Stochastic Mode Reduction
Plug into equation for x:
CAM Noise
Cubic Term
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Outline
1) Motivation
2) Stochastic Mode Reduction
3) Physical Constraints
4) Bayesian Parameter Estimation
5) Results
6) Summary
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Constraints on Stochastic Climate Models
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Constraints on Stochastic Climate Models
Only considering cubic terms
Majda et al. 2009; Peavoy et al. 2015
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Constraints on Stochastic Climate Models
Majda et al. 2009; Peavoy et al. 2015
Stability:
Quadratic form Q is negativedefinite
Allows the system to be linearly unstable
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Peavoy et al. 2015
Physical Constraints
Without constraint about40% of parameterestimates lead to unstablesolutions
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Outline
1) Motivation
2) Stochastic Mode Reduction
3) Physical Constraints
4) Bayesian Parameter Estimation
5) Results
6) Summary
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Bayesian Parameter Estimation Procedure
Imputing of Data
Discretisation: EulerMaruyama scheme
Modified Linear Bridge
Likelihood based parameter estimation (MCMC)
Peavoy et al. 2015
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Peavoy et al. 2015
How to sample negativedefinite matrices?
Wishart Distribution
Truncated Normal Algorithm:A n×n matrix is negative definite if and only if all k≤n
leading principal minors obey |Mk|(1)k > 0. The kth principal minor is the determinant of the upperleft k×k submatrix.
Diagonal ElementsOffDiagonal Elements
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Peavoy et al. 2015
Modelling Memory Effects via Latent Variables
Red Noise
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Outline
1) Motivation
2) Stochastic Mode Reduction
3) Physical Constraints
4) Bayesian Parameter Estimation
5) Results
6) Summary
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Triad Model Example
ModelReduction
Peavoy et al. 2015
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Triad Model Example
Peavoy et al. 2015
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Test: Chaotic Lorenz Model
ε = 0.1 ε = 0.01
Reduced order model:
Peavoy et al. 2015
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Peavoy et al. 2015
Flow over topography on a ßplane
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Arctic Oscillation Index
Autocorrelation Function
From NCARNCEP reanalysis datacovering period 19482010
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● North Atlantic Jet Stream has three persistent states
● Persistent states exhibit variability on interannual and decadal time scales
● Propensity of extreme wind speeds depend on persistent states
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Summary● Normal form for reduced stochastic climate models
predict a cubic nonlinear drift and a correlated additive and multiplicative CAM noise.
● Bayesian Framework for Physics Constrained Parameter Estimation
● Reduced stochastic climate models perform wellReferences:● Majda, Franzke and Crommelin, 2009: Normal forms for reduced stochastic
climate models. Proc. Natl. Acad. Sci. USA, 106, 36493653.● Peavoy, Franzke and Roberts, 2015: Physics constrained parameter estimation of
stochastic differential equations. Comp. Stat. Data Ana., 83, 182199.● Franzke, C., T. O'Kane, J. Berner, P. Williams and V. Lucarini, 2015: Stochastic
Climate Theory and Modelling. WIREs Climate Change, 6, 6378.● Gottwald, G., D. Crommelin and C. Franzke, 2016: Stochastic Climate Theory. To
appear in Nonlinear and Stochastic Climate Dynamics, Cambridge University Press.