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Modelling Interactions BetweenWeather and Climate
Adam Monahan & Joel [email protected] & [email protected]
School of Earth and Ocean Sciences, University of Victoria
Victoria, BC, Canada
Modelling Interactions Between Weather and Climate – p. 1/33
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Introduction
Earth’s climate system displays variability on many scales
Modelling Interactions Between Weather and Climate – p. 2/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
Modelling Interactions Between Weather and Climate – p. 2/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Modelling Interactions Between Weather and Climate – p. 2/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Loosely, slower variability called “climate” and faster called “weather”
Modelling Interactions Between Weather and Climate – p. 2/33
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Introduction
From von Storch and Zwiers, 1999Modelling Interactions Between Weather and Climate – p. 3/33
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Introduction
From von Storch and Zwiers, 1999
Modelling Interactions Between Weather and Climate – p. 4/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Loosely, slower variability called “climate” and faster called “weather”
Dynamical nonlinearities⇒ coupling across space/time scales
Modelling Interactions Between Weather and Climate – p. 5/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Loosely, slower variability called “climate” and faster called “weather”
Dynamical nonlinearities⇒ coupling across space/time scales
⇒ modelling climate, weather must be parameterised, not ignored
Modelling Interactions Between Weather and Climate – p. 5/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Loosely, slower variability called “climate” and faster called “weather”
Dynamical nonlinearities⇒ coupling across space/time scales
⇒ modelling climate, weather must be parameterised, not ignored
Classical deterministic parameterisations assume very large scale separation
Modelling Interactions Between Weather and Climate – p. 5/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Loosely, slower variability called “climate” and faster called “weather”
Dynamical nonlinearities⇒ coupling across space/time scales
⇒ modelling climate, weather must be parameterised, not ignored
Classical deterministic parameterisations assume very large scale separation
Following Mitchell (1966), Hasselmann (1976) suggestedstochastic
parameterisations more appropriate when separation finite
Modelling Interactions Between Weather and Climate – p. 5/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Loosely, slower variability called “climate” and faster called “weather”
Dynamical nonlinearities⇒ coupling across space/time scales
⇒ modelling climate, weather must be parameterised, not ignored
Classical deterministic parameterisations assume very large scale separation
Following Mitchell (1966), Hasselmann (1976) suggestedstochastic
parameterisations more appropriate when separation finite
This talk will consider:
Modelling Interactions Between Weather and Climate – p. 5/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Loosely, slower variability called “climate” and faster called “weather”
Dynamical nonlinearities⇒ coupling across space/time scales
⇒ modelling climate, weather must be parameterised, not ignored
Classical deterministic parameterisations assume very large scale separation
Following Mitchell (1966), Hasselmann (1976) suggestedstochastic
parameterisations more appropriate when separation finite
This talk will consider:
stochastic averagingas a tool for obtaining stochastic climate models
from coupled weather/climate system
Modelling Interactions Between Weather and Climate – p. 5/33
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Introduction
Earth’s climate system displays variability on many scales
space:10−6m - 107 m
time: seconds -106 years (and beyond)
Loosely, slower variability called “climate” and faster called “weather”
Dynamical nonlinearities⇒ coupling across space/time scales
⇒ modelling climate, weather must be parameterised, not ignored
Classical deterministic parameterisations assume very large scale separation
Following Mitchell (1966), Hasselmann (1976) suggestedstochastic
parameterisations more appropriate when separation finite
This talk will consider:
stochastic averagingas a tool for obtaining stochastic climate models
from coupled weather/climate system
two examples: coupled atmosphere/ocean boundary layers,
extratropical atmospheric low-frequency variabilityModelling Interactions Between Weather and Climate – p. 5/33
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A statistical mechanical perspective
Consider a box at temperatureT containingN ideal gas molecules
Modelling Interactions Between Weather and Climate – p. 6/33
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A statistical mechanical perspective
Consider a box at temperatureT containingN ideal gas molecules
Kinetic energy of individual molecules randomly distributed with
Maxwell-Boltzmann distributionp(E)
Modelling Interactions Between Weather and Climate – p. 6/33
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A statistical mechanical perspective
Consider a box at temperatureT containingN ideal gas molecules
Kinetic energy of individual molecules randomly distributed with
Maxwell-Boltzmann distributionp(E)
AsN → ∞ mean energy approaches sharp value
E =1
N
N∑
i=1
Ei
−→N → ∞ E =
∫
E p(E) dE
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A statistical mechanical perspective
Consider a box at temperatureT containingN ideal gas molecules
Kinetic energy of individual molecules randomly distributed with
Maxwell-Boltzmann distributionp(E)
AsN → ∞ mean energy approaches sharp value
E =1
N
N∑
i=1
Ei
−→N → ∞ E =
∫
E p(E) dE
ForN large (but finite),E is random
E = E +1√Nζ
such that (by central limit theorem)ζ is Gaussian
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A statistical mechanical perspective
Consider a box at temperatureT containingN ideal gas molecules
Kinetic energy of individual molecules randomly distributed with
Maxwell-Boltzmann distributionp(E)
AsN → ∞ mean energy approaches sharp value
E =1
N
N∑
i=1
Ei
−→N → ∞ E =
∫
E p(E) dE
ForN large (but finite),E is random
E = E +1√Nζ
such that (by central limit theorem)ζ is Gaussian
Very large scale separation⇒ deterministic dynamics
Modelling Interactions Between Weather and Climate – p. 6/33
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A statistical mechanical perspective
Consider a box at temperatureT containingN ideal gas molecules
Kinetic energy of individual molecules randomly distributed with
Maxwell-Boltzmann distributionp(E)
AsN → ∞ mean energy approaches sharp value
E =1
N
N∑
i=1
Ei
−→N → ∞ E =
∫
E p(E) dE
ForN large (but finite),E is random
E = E +1√Nζ
such that (by central limit theorem)ζ is Gaussian
Very large scale separation⇒ deterministic dynamics
Smaller (but still large) separation⇒ stochastic corrections needed
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Hasselmann’s ansatz: a stochastic climate model
Observation: spectra of sea surface temperature (SST) variability are
generically red
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Hasselmann’s ansatz: a stochastic climate model
Observation: spectra of sea surface temperature (SST) variability are
generically red
Observations from North Atlantic weathership “INDIA”
From Frankignoul & HasselmannTellus1977
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Hasselmann’s ansatz: a stochastic climate model
Simple model of Frankignoul & Hasselmann (1977) assumed:
Modelling Interactions Between Weather and Climate – p. 8/33
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Hasselmann’s ansatz: a stochastic climate model
Simple model of Frankignoul & Hasselmann (1977) assumed:
local dynamics of small perturbationsT ′ linearised around
climatological mean state
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Hasselmann’s ansatz: a stochastic climate model
Simple model of Frankignoul & Hasselmann (1977) assumed:
local dynamics of small perturbationsT ′ linearised around
climatological mean state
“fast” atmospheric fluxes represented as white noise
Modelling Interactions Between Weather and Climate – p. 8/33
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Hasselmann’s ansatz: a stochastic climate model
Simple model of Frankignoul & Hasselmann (1977) assumed:
local dynamics of small perturbationsT ′ linearised around
climatological mean state
“fast” atmospheric fluxes represented as white noise
⇒ simple Ornstein-Uhlenbeck process
dT ′
dt= −1
τT ′ + γW
with spectrumE{T ′(ω)2} =
γ2τ2
2π(1 + ω2τ2)
Modelling Interactions Between Weather and Climate – p. 8/33
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Hasselmann’s ansatz: a stochastic climate model
Simple model of Frankignoul & Hasselmann (1977) assumed:
local dynamics of small perturbationsT ′ linearised around
climatological mean state
“fast” atmospheric fluxes represented as white noise
⇒ simple Ornstein-Uhlenbeck process
dT ′
dt= −1
τT ′ + γW
with spectrumE{T ′(ω)2} =
γ2τ2
2π(1 + ω2τ2)
Linear stochastic model
⇒ simple null hypothesis for observed variability
Modelling Interactions Between Weather and Climate – p. 8/33
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Hasselmann’s ansatz: a stochastic climate model
Simple model of Frankignoul & Hasselmann (1977) assumed:
local dynamics of small perturbationsT ′ linearised around
climatological mean state
“fast” atmospheric fluxes represented as white noise
⇒ simple Ornstein-Uhlenbeck process
dT ′
dt= −1
τT ′ + γW
with spectrumE{T ′(ω)2} =
γ2τ2
2π(1 + ω2τ2)
Linear stochastic model
⇒ simple null hypothesis for observed variability
We’ll be coming back to this again later on ....
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Coupled fast-slow systems
Starting point for stochastic averaging is assumption thatstate
variablez can be written
z = (x,y)
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Coupled fast-slow systems
Starting point for stochastic averaging is assumption thatstate
variablez can be written
z = (x,y)
wherex ∼ “slow variable” (climate)
y ∼ “fast variable” (weather)such that
d
dtx = f(x,y)
d
dty = g(x,y)
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Coupled fast-slow systems
Starting point for stochastic averaging is assumption thatstate
variablez can be written
z = (x,y)
wherex ∼ “slow variable” (climate)
y ∼ “fast variable” (weather)such that
d
dtx = f(x,y)
d
dty = g(x,y)
whereτ ∼ f(x,y)
g(x,y)
is “small"
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A simple example
As a simple example of a coupled fast-slow system consider
d
dtx = x− x3 + (Σ + x)y
d
dty = − 1
τ |x|y +1√τW
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A simple example
As a simple example of a coupled fast-slow system consider
d
dtx = x− x3 + (Σ + x)y
d
dty = − 1
τ |x|y +1√τW
The “weather” processy is Gaussian with stationary autocovariance
Cyy(s) = E {y(t)y(t+ s)} =|x|2
exp
(
− |s|τ |x|
)
−→asτ → 0 τx2δ(s)
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A simple example
As a simple example of a coupled fast-slow system consider
d
dtx = x− x3 + (Σ + x)y
d
dty = − 1
τ |x|y +1√τW
The “weather” processy is Gaussian with stationary autocovariance
Cyy(s) = E {y(t)y(t+ s)} =|x|2
exp
(
− |s|τ |x|
)
−→asτ → 0 τx2δ(s)
Intuition suggests that asτ → 0
d
dtx ≃ x− x3 +
√τ(Σ + x)|x| ◦ W
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Averaging approximation (A)
As τ → 0, over finite times we havex(t) ≃ x(t)
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Averaging approximation (A)
As τ → 0, over finite times we havex(t) ≃ x(t)
with d
dtx = f(x)
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Averaging approximation (A)
As τ → 0, over finite times we havex(t) ≃ x(t)
with d
dtx = f(x)
where
f(x) =
∫
f(x,y)dµx(y) = limT→∞
1
T
∫ T
0
f(x,y(t)) dt
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Averaging approximation (A)
As τ → 0, over finite times we havex(t) ≃ x(t)
with d
dtx = f(x)
where
f(x) =
∫
f(x,y)dµx(y) = limT→∞
1
T
∫ T
0
f(x,y(t)) dt
averagesf(x,y) over weather for a fixed climate state
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Averaging approximation (A)
As τ → 0, over finite times we havex(t) ≃ x(t)
with d
dtx = f(x)
where
f(x) =
∫
f(x,y)dµx(y) = limT→∞
1
T
∫ T
0
f(x,y(t)) dt
averagesf(x,y) over weather for a fixed climate state
To leading order, “climate” follows deterministic averaged dynamics
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Averaging approximation (A)
As τ → 0, over finite times we havex(t) ≃ x(t)
with d
dtx = f(x)
where
f(x) =
∫
f(x,y)dµx(y) = limT→∞
1
T
∫ T
0
f(x,y(t)) dt
averagesf(x,y) over weather for a fixed climate state
To leading order, “climate” follows deterministic averaged dynamics
For simple model
d
dtx = Ey|x
{
x− x3 + xy + Σy}
= x− x3
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Averaging approximation (A)
As τ → 0, over finite times we havex(t) ≃ x(t)
with d
dtx = f(x)
where
f(x) =
∫
f(x,y)dµx(y) = limT→∞
1
T
∫ T
0
f(x,y(t)) dt
averagesf(x,y) over weather for a fixed climate state
To leading order, “climate” follows deterministic averaged dynamics
For simple model
d
dtx = Ey|x
{
x− x3 + xy + Σy}
= x− x3
i.e. depending onx(0) system settles into bottom of one of two
potential wellsx = ±1Modelling Interactions Between Weather and Climate – p. 11/33
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Analogy to Reynolds averaging
Approximation (A) analogous to Reynolds averaging in fluid
mechanics: split flow into “mean” and “turbulent” components
u = u+ u′
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Analogy to Reynolds averaging
Approximation (A) analogous to Reynolds averaging in fluid
mechanics: split flow into “mean” and “turbulent” components
u = u+ u′
whereu(t) =
1
2T
∫ T
−T
u(t+ s) ds
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Analogy to Reynolds averaging
Approximation (A) analogous to Reynolds averaging in fluid
mechanics: split flow into “mean” and “turbulent” components
u = u+ u′
whereu(t) =
1
2T
∫ T
−T
u(t+ s) ds
Full flow satisfies Navier-Stokes equations
∂tu+ u · ∇u = force
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Analogy to Reynolds averaging
Approximation (A) analogous to Reynolds averaging in fluid
mechanics: split flow into “mean” and “turbulent” components
u = u+ u′
whereu(t) =
1
2T
∫ T
−T
u(t+ s) ds
Full flow satisfies Navier-Stokes equations
∂tu+ u · ∇u = force
⇒ mean flow satisfies “eddy averaged” dynamics
∂tu+ u · ∇u = force− u′ · ∇u′
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Analogy to Reynolds averaging
Approximation (A) analogous to Reynolds averaging in fluid
mechanics: split flow into “mean” and “turbulent” components
u = u+ u′
whereu(t) =
1
2T
∫ T
−T
u(t+ s) ds
Full flow satisfies Navier-Stokes equations
∂tu+ u · ∇u = force
⇒ mean flow satisfies “eddy averaged” dynamics
∂tu+ u · ∇u = force− u′ · ∇u′
Note: averaging is projection operator only if scale separation
betweenu andu′
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Central limit theorem approximation (L)
For τ 6= 0 averaged and full trajectories differ; this difference canbe
modelled as a random process
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Central limit theorem approximation (L)
For τ 6= 0 averaged and full trajectories differ; this difference canbe
modelled as a random process
Basic result: forτ small x(t) ≃ x(t) + ζ
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Central limit theorem approximation (L)
For τ 6= 0 averaged and full trajectories differ; this difference canbe
modelled as a random process
Basic result: forτ small x(t) ≃ x(t) + ζ
whereζ is the multivariate Ornstein-Uhlenbeck process
d
dtζ = Df(x) ζ + σ(x)W
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Central limit theorem approximation (L)
For τ 6= 0 averaged and full trajectories differ; this difference canbe
modelled as a random process
Basic result: forτ small x(t) ≃ x(t) + ζ
whereζ is the multivariate Ornstein-Uhlenbeck process
d
dtζ = Df(x) ζ + σ(x)W
Diffusion matrixσ(x) defined by lag correlation integrals
σ2(x) =
∫ ∞
−∞
Ey|x {f ′[x,y(t+ s)]f ′[x,y(t)]} ds
where f ′[x,y(t)] = f [x,y(t)] − f [x]
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Central limit theorem approximation (L)
For τ 6= 0 averaged and full trajectories differ; this difference canbe
modelled as a random process
Basic result: forτ small x(t) ≃ x(t) + ζ
whereζ is the multivariate Ornstein-Uhlenbeck process
d
dtζ = Df(x) ζ + σ(x)W
Diffusion matrixσ(x) defined by lag correlation integrals
σ2(x) =
∫ ∞
−∞
Ey|x {f ′[x,y(t+ s)]f ′[x,y(t)]} ds
where f ′[x,y(t)] = f [x,y(t)] − f [x]
Note that std(ζ) ∼ √τ so corrections vanish in strictτ → 0 limit
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Central limit theorem approximation (L)
Averaging solutionx(t) augmented by Gaussian “corrections” such that
x(t) itself not affected
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Central limit theorem approximation (L)
Averaging solutionx(t) augmented by Gaussian “corrections” such that
x(t) itself not affected
⇒ feedback of weather on climate only through averaging; weather can’t drive
climate between metastable states
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Central limit theorem approximation (L)
Averaging solutionx(t) augmented by Gaussian “corrections” such that
x(t) itself not affected
⇒ feedback of weather on climate only through averaging; weather can’t drive
climate between metastable states
(L) approximation still has been very successful in many contexts (e.g.
Linear Inverse Modelling)
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Central limit theorem approximation (L)
Averaging solutionx(t) augmented by Gaussian “corrections” such that
x(t) itself not affected
⇒ feedback of weather on climate only through averaging; weather can’t drive
climate between metastable states
(L) approximation still has been very successful in many contexts (e.g.
Linear Inverse Modelling)
For the simple example model:
f ′(x, y) = (x− x3 + xy + Σy) − (x− x3) = (x+ Σ)y
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Central limit theorem approximation (L)
Averaging solutionx(t) augmented by Gaussian “corrections” such that
x(t) itself not affected
⇒ feedback of weather on climate only through averaging; weather can’t drive
climate between metastable states
(L) approximation still has been very successful in many contexts (e.g.
Linear Inverse Modelling)
For the simple example model:
f ′(x, y) = (x− x3 + xy + Σy) − (x− x3) = (x+ Σ)y
soσ2(x) = (x+ Σ)2
∫ ∞
−∞
Ey|x {y(t+ s)y(t)} ds = (x+ Σ)2x2
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Central limit theorem approximation (L)
Averaging solutionx(t) augmented by Gaussian “corrections” such that
x(t) itself not affected
⇒ feedback of weather on climate only through averaging; weather can’t drive
climate between metastable states
(L) approximation still has been very successful in many contexts (e.g.
Linear Inverse Modelling)
For the simple example model:
f ′(x, y) = (x− x3 + xy + Σy) − (x− x3) = (x+ Σ)y
soσ2(x) = (x+ Σ)2
∫ ∞
−∞
Ey|x {y(t+ s)y(t)} ds = (x+ Σ)2x2
⇒ x(t) → 1 + ζ such thatd
dtζ = −2ζ + (1 + Σ)2W
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Hasselmann approximation (N)
More complete approximation involves full two-way interaction between
weather and climate
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Hasselmann approximation (N)
More complete approximation involves full two-way interaction between
weather and climate
x(t) solution of stochastic differential equation (SDE)
d
dtx = f(x) + σ(x) ◦ W
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Hasselmann approximation (N)
More complete approximation involves full two-way interaction between
weather and climate
x(t) solution of stochastic differential equation (SDE)
d
dtx = f(x) + σ(x) ◦ W
Most accurate of all approximations on long timescales; cancapture
transitions between metastable states
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Hasselmann approximation (N)
More complete approximation involves full two-way interaction between
weather and climate
x(t) solution of stochastic differential equation (SDE)
d
dtx = f(x) + σ(x) ◦ W
Most accurate of all approximations on long timescales; cancapture
transitions between metastable states
Noise-induced drift⇒ further potential feedback of weather on climate
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Hasselmann approximation (N)
More complete approximation involves full two-way interaction between
weather and climate
x(t) solution of stochastic differential equation (SDE)
d
dtx = f(x) + σ(x) ◦ W
Most accurate of all approximations on long timescales; cancapture
transitions between metastable states
Noise-induced drift⇒ further potential feedback of weather on climate
Simple model: d
dtx = x− x3 + (Σ + x)|x| ◦ W
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Hasselmann approximation (N)
More complete approximation involves full two-way interaction between
weather and climate
x(t) solution of stochastic differential equation (SDE)
d
dtx = f(x) + σ(x) ◦ W
Most accurate of all approximations on long timescales; cancapture
transitions between metastable states
Noise-induced drift⇒ further potential feedback of weather on climate
Simple model: d
dtx = x− x3 + (Σ + x)|x| ◦ W
Multiplicative noise introduced through:
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Hasselmann approximation (N)
More complete approximation involves full two-way interaction between
weather and climate
x(t) solution of stochastic differential equation (SDE)
d
dtx = f(x) + σ(x) ◦ W
Most accurate of all approximations on long timescales; cancapture
transitions between metastable states
Noise-induced drift⇒ further potential feedback of weather on climate
Simple model: d
dtx = x− x3 + (Σ + x)|x| ◦ W
Multiplicative noise introduced through:
nonlinear coupling ofx andy in slow dynamics
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Hasselmann approximation (N)
More complete approximation involves full two-way interaction between
weather and climate
x(t) solution of stochastic differential equation (SDE)
d
dtx = f(x) + σ(x) ◦ W
Most accurate of all approximations on long timescales; cancapture
transitions between metastable states
Noise-induced drift⇒ further potential feedback of weather on climate
Simple model: d
dtx = x− x3 + (Σ + x)|x| ◦ W
Multiplicative noise introduced through:
nonlinear coupling ofx andy in slow dynamics
dependence of stationary distribution ofy onx
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When does this work?
For the (A) approximation to hold all that is required is a timescale
separation
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When does this work?
For the (A) approximation to hold all that is required is a timescale
separation
For (L) and (N) further require fast process isstrongly mixing
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When does this work?
For the (A) approximation to hold all that is required is a timescale
separation
For (L) and (N) further require fast process isstrongly mixing
Informally, what is required is that the autocorrelation function ofy
decay sufficiently rapidly with lag for large timescale separations the
delta-correlated white noise approximation is reasonable
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When does this work?
For the (A) approximation to hold all that is required is a timescale
separation
For (L) and (N) further require fast process isstrongly mixing
Informally, what is required is that the autocorrelation function ofy
decay sufficiently rapidly with lag for large timescale separations the
delta-correlated white noise approximation is reasonable
Crucially : none of this assumes that the fast process is stochastic.
Effective SDEs (L) and (N) arise in limitτ → 0 as approximation to
fastdeterministic or stochasticdynamics
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Relation to “MTV Theory"
Have assumed that influence of weather on climate is bounded as τ → 0
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Relation to “MTV Theory"
Have assumed that influence of weather on climate is bounded as τ → 0
“MTV" theory considers averaging assuming that weather influence
strengthensin this limit
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Relation to “MTV Theory"
Have assumed that influence of weather on climate is bounded as τ → 0
“MTV" theory considers averaging assuming that weather influence
strengthensin this limit
An example:dx
dt= −x+
a√τy
dy
dt=
1√τx− 1
τy +
b√τW
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Relation to “MTV Theory"
Have assumed that influence of weather on climate is bounded as τ → 0
“MTV" theory considers averaging assuming that weather influence
strengthensin this limit
An example:dx
dt= −x+
a√τy
dy
dt=
1√τx− 1
τy +
b√τW
Ey|x {y} =√τx , stdy|x = b2/2
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Relation to “MTV Theory"
Have assumed that influence of weather on climate is bounded as τ → 0
“MTV" theory considers averaging assuming that weather influence
strengthensin this limit
An example:dx
dt= −x+
a√τy
dy
dt=
1√τx− 1
τy +
b√τW
Ey|x {y} =√τx , stdy|x = b2/2
Stochastic terms remain in the strictτ → 0 limit
dx
dt= −(1 − a)x+ abW
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Relation to “MTV Theory"
Have assumed that influence of weather on climate is bounded as τ → 0
“MTV" theory considers averaging assuming that weather influence
strengthensin this limit
An example:dx
dt= −x+
a√τy
dy
dt=
1√τx− 1
τy +
b√τW
Ey|x {y} =√τx , stdy|x = b2/2
Stochastic terms remain in the strictτ → 0 limit
dx
dt= −(1 − a)x+ abW
Present analysis will not make MTV ansatz of increasing weather influence
asτ decreases; rather than a “τ = 0 theory", it is a “smallτ theory"
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Case study 1: Coupled atmosphere-ocean boundary layers
Atmosphere and ocean interact through (generally turbulent) boundary
layers, exchanging mass, momentum, and energy
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Case study 1: Coupled atmosphere-ocean boundary layers
Atmosphere and ocean interact through (generally turbulent) boundary
layers, exchanging mass, momentum, and energy
Idealised coupled model of atmospheric winds and air/sea temperatures:
d
dtu = 〈Πu〉 −
cd(w)
h(Ta, To)wu− we
h(Ta, To)u+ σuW1
d
dtv = − cd(w)
h(Ta, To)wv − we
h(Ta, To)v + σuW2
d
dtTa =
β
γa
(To − Ta)w − β
γa
θ(w − µw) − λa
γa
Ta + Σ11W3 + Σ12W4
d
dtTo =
β
γo
(Ta − To)w +β
γo
θ(w − µw) − λo
γo
To + Σ21W3 + Σ22W4
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Case study 1: Coupled atmosphere-ocean boundary layers
165oE 180oW 165oW 150oW 135oW 120oW 30oN
36oN
42oN
48oN
54oN
60oN
OSP
Ocean station P
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Case study 1: Coupled atmosphere-ocean boundary layers
Atmosphere and ocean interact through (generally turbulent) boundary
layers, exchanging mass, momentum, and energy
Idealised coupled model of atmospheric winds and air/sea temperatures:
d
dtu = 〈Πu〉 −
cd(w)
h(Ta, To)wu− we
h(Ta, To)u+ σuW1
d
dtv = − cd(w)
h(Ta, To)wv − we
h(Ta, To)v + σuW2
d
dtTa =
β
γa
(To − Ta)w − β
γa
θ(w − µw) − λa
γa
Ta + Σ11W3 + Σ12W4
d
dtTo =
β
γo
(Ta − To)w +β
γo
θ(w − µw) − λo
γo
To + Σ21W3 + Σ22W4
Atmospheric boundary layer thickness determined by surface stratification
h(Ta, To) = max[
hmin, h(1 − α(Ta − To))]
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Case study 1: Coupled atmosphere-ocean boundary layers
−3 −2 −1 0 1 2 3400
500
600
700
800
900
1000
1100
Ta−T
o (K)
h (m
)
Boundary layer height and surface stability
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Case study 1: Coupled atmosphere-ocean boundary layers
0 20 40 60 80
0
0.5
1
lag (days)
corr
elat
ion
T
a (obs)
Ta (sim)
To (obs)
To (sim)
Ta (K)
To (
K)
−4 −2 0 2 4−2
−1
0
1
2
ObsSim
0 2 4 6−0.2
0
0.2
0.4
0.6
0.8
lag (days)
corr
elat
ion
ObsSim
0 10 20 300
0.02
0.04
0.06
0.08
0.1
w (m/s)
p(w
)
ObsSim
Observed and simulated statistics (Ocean Station P)Modelling Interactions Between Weather and Climate – p. 22/33
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Case study 1: Coupled atmosphere-ocean boundary layers
Observed timescale separations:
τwτTa
∼ 0.7
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Case study 1: Coupled atmosphere-ocean boundary layers
Observed timescale separations:
τwτTa
∼ 0.7
Takex = T = (Ta, To), y = (u, v) with
Modelling Interactions Between Weather and Climate – p. 23/33
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Case study 1: Coupled atmosphere-ocean boundary layers
Observed timescale separations:
τwτTa
∼ 0.7
Takex = T = (Ta, To), y = (u, v) with
Averaging model (A)d
dtT = f(T) + ΣW
Ta (K)
To (
K)
f1( K / s )
−5 0 5−5
0
5
−5 0 5 10 15
x 10−5
Ta (K)
f2( K / s )
−5 0 5−5
0
5
−5 0 5
x 10−6
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Case study 1: Coupled atmosphere-ocean boundary layers
Analytic expression forσ(Ta, To)
σ(Ta, To) =β
√
γ2a + γ2
o
γo
γa
−1
−1 γa
γo
ψ(Ta − To)
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Case study 1: Coupled atmosphere-ocean boundary layers
Analytic expression forσ(Ta, To)
σ(Ta, To) =β
√
γ2a + γ2
o
γo
γa
−1
−1 γa
γo
ψ(Ta − To)
whereψ(Ta − To) = (Ta − To + θ)
√
∫ ∞
−∞
E {w′(t+ s)w′(t)} ds
Modelling Interactions Between Weather and Climate – p. 24/33
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Case study 1: Coupled atmosphere-ocean boundary layers
Analytic expression forσ(Ta, To)
σ(Ta, To) =β
√
γ2a + γ2
o
γo
γa
−1
−1 γa
γo
ψ(Ta − To)
whereψ(Ta − To) = (Ta − To + θ)
√
∫ ∞
−∞
E {w′(t+ s)w′(t)} ds
−10 −5 0 5 10−2.5
−2
−1.5
−1
−0.5
0
0.5
Ta−T
o (K)
ψ (
K m
s−
1/2 ×
104 )
Modelling Interactions Between Weather and Climate – p. 24/33
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Case study 1: Coupled atmosphere-ocean boundary layers
0 20 40 60 80
0
0.5
1
lag (days)
corr
elat
ion
T
a (reduced)
Ta (full)
To (reduced)
To (full)
Ta (K)
To (
K)
−4 −2 0 2 4−2
−1
0
1
2
ReducedFull
τ = 0.7
0 20 40 60 80
0
0.5
1
lag (days)
corr
elat
ion
T
a (reduced)
Ta (full)
To (reduced)
To (full)
Ta (K)
To (
K)
−4 −2 0 2 4−2
−1
0
1
2
ReducedFull
τ = 0.35
Modelling Interactions Between Weather and Climate – p. 25/33
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Case study 1: Coupled atmosphere-ocean boundary layers
Sura and Newman (2008) fit observations at OSP to linear
multiplicative SDE
d
dtT = AT +B(T) ◦ W
Modelling Interactions Between Weather and Climate – p. 26/33
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Case study 1: Coupled atmosphere-ocean boundary layers
Sura and Newman (2008) fit observations at OSP to linear
multiplicative SDE
d
dtT = AT +B(T) ◦ W
Form of this SDE justified through simple substitution
w → µw + σW
in full coupled dynamics
Modelling Interactions Between Weather and Climate – p. 26/33
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Case study 1: Coupled atmosphere-ocean boundary layers
Sura and Newman (2008) fit observations at OSP to linear
multiplicative SDE
d
dtT = AT +B(T) ◦ W
Form of this SDE justified through simple substitution
w → µw + σW
in full coupled dynamics
Form of SDE did not account for feedback of stratification onh;
physical origin of parameters in inverse model somewhat different
than had been assumed
Modelling Interactions Between Weather and Climate – p. 26/33
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Case study 2: extratropical atmospheric LFV
Variability of extratropical atmosphere on timescales of∼ 10 days +
descrbed as “low-frequency variability” (LFV)
Modelling Interactions Between Weather and Climate – p. 27/33
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Case study 2: extratropical atmospheric LFV
Variability of extratropical atmosphere on timescales of∼ 10 days +
descrbed as “low-frequency variability” (LFV)
Associated with hemispheric to global spatial scales
Modelling Interactions Between Weather and Climate – p. 27/33
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Case study 2: extratropical atmospheric LFV
Variability of extratropical atmosphere on timescales of∼ 10 days +
descrbed as “low-frequency variability” (LFV)
Associated with hemispheric to global spatial scales
Northern Annular Mode (NAM)
Fromwww.whoi.edu
Modelling Interactions Between Weather and Climate – p. 27/33
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Case study 2: extratropical atmospheric LFV
Variability of extratropical atmosphere on timescales of∼ 10 days +
descrbed as “low-frequency variability” (LFV)
Associated with hemispheric to global spatial scales
Northern Annular Mode (NAM)
Fromwww.whoi.edu
Basic structures characterised
statistically; dynamical questions
remain open
Modelling Interactions Between Weather and Climate – p. 27/33
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Case study 2: extratropical atmospheric LFV
Variability of extratropical atmosphere on timescales of∼ 10 days +
descrbed as “low-frequency variability” (LFV)
Associated with hemispheric to global spatial scales
Northern Annular Mode (NAM)
Fromwww.whoi.edu
Basic structures characterised
statistically; dynamical questions
remain open
Empirical stochastic models have
been useful for studying LFV
Modelling Interactions Between Weather and Climate – p. 27/33
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Case study 2: extratropical atmospheric LFV
Variability of extratropical atmosphere on timescales of∼ 10 days +
descrbed as “low-frequency variability” (LFV)
Associated with hemispheric to global spatial scales
Northern Annular Mode (NAM)
Fromwww.whoi.edu
Basic structures characterised
statistically; dynamical questions
remain open
Empirical stochastic models have
been useful for studying LFV
Stochastic reduction techniques⇒natural tool for investigating
dynamics
Modelling Interactions Between Weather and Climate – p. 27/33
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Case study 2: extratropical atmospheric LFV
Kravtsov et al. JAS 2005Modelling Interactions Between Weather and Climate – p. 28/33
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Case study 2: extratropical atmospheric LFV
Using PCA decomposition, identify 2 slow modes: propagating
wavenumber 4, stationary zonal
Modelling Interactions Between Weather and Climate – p. 29/33
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Case study 2: extratropical atmospheric LFV
Using PCA decomposition, identify 2 slow modes: propagating
wavenumber 4, stationary zonal
Kravtsov et al. (2005) concluded that regime behaviour resulted from
nonlinear interaction of these modes
Modelling Interactions Between Weather and Climate – p. 29/33
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Case study 2: extratropical atmospheric LFV
Using PCA decomposition, identify 2 slow modes: propagating
wavenumber 4, stationary zonal
Kravtsov et al. (2005) concluded that regime behaviour resulted from
nonlinear interaction of these modes
Consider reduction to 3-variable, 1-variable (stationarymode) models
Modelling Interactions Between Weather and Climate – p. 29/33
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Case study 2: extratropical atmospheric LFV
Modelling Interactions Between Weather and Climate – p. 30/33
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Case study 2: extratropical atmospheric LFV
Modelling Interactions Between Weather and Climate – p. 31/33
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Case study 2: extratropical atmospheric LFV
Stationary mode ”potential”
shows prominent shoulder
Modelling Interactions Between Weather and Climate – p. 31/33
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Case study 2: extratropical atmospheric LFV
Stationary mode ”potential”
shows prominent shoulder
In this model, appears that
multiple regimes produced by
nonlinearity in effective
dynamics of stationary mode
Modelling Interactions Between Weather and Climate – p. 31/33
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Case study 2: extratropical atmospheric LFV
Stationary mode ”potential”
shows prominent shoulder
In this model, appears that
multiple regimes produced by
nonlinearity in effective
dynamics of stationary mode
Non-gaussianity doesn’t require
state-dependent noise
Modelling Interactions Between Weather and Climate – p. 31/33
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Case study 2: extratropical atmospheric LFV
MTV approximations with “minimal regression” tuning
Modelling Interactions Between Weather and Climate – p. 32/33
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Conclusions
Stochastic reduction tools allow systematic derivation oflow-order
climate models from coupled weather/climate systems
Modelling Interactions Between Weather and Climate – p. 33/33
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Conclusions
Stochastic reduction tools allow systematic derivation oflow-order
climate models from coupled weather/climate systems
Reduction approach is straightforward, although implementation in
high-dimensional systems challenging
Modelling Interactions Between Weather and Climate – p. 33/33
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Conclusions
Stochastic reduction tools allow systematic derivation oflow-order
climate models from coupled weather/climate systems
Reduction approach is straightforward, although implementation in
high-dimensional systems challenging
This approach provides a more general approach than e.g. MTV
theory, with
Modelling Interactions Between Weather and Climate – p. 33/33
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Conclusions
Stochastic reduction tools allow systematic derivation oflow-order
climate models from coupled weather/climate systems
Reduction approach is straightforward, although implementation in
high-dimensional systems challenging
This approach provides a more general approach than e.g. MTV
theory, with
benefitof less restrictive assumptions
Modelling Interactions Between Weather and Climate – p. 33/33
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Conclusions
Stochastic reduction tools allow systematic derivation oflow-order
climate models from coupled weather/climate systems
Reduction approach is straightforward, although implementation in
high-dimensional systems challenging
This approach provides a more general approach than e.g. MTV
theory, with
benefitof less restrictive assumptions
drawbacksof more difficult implementation, lack of closed-form
solution
Modelling Interactions Between Weather and Climate – p. 33/33
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Conclusions
Stochastic reduction tools allow systematic derivation oflow-order
climate models from coupled weather/climate systems
Reduction approach is straightforward, although implementation in
high-dimensional systems challenging
This approach provides a more general approach than e.g. MTV
theory, with
benefitof less restrictive assumptions
drawbacksof more difficult implementation, lack of closed-form
solution
Real systems do not generally have a strong scale separation; an
important outstanding problem is a more systematic approach to
accounting for this
Modelling Interactions Between Weather and Climate – p. 33/33