Modelling Interactions Between Weather and Climate
|
|
- Cory McCoy
- 6 years ago
- Views:
Transcription
1 Modelling Interactions Between Weather and Climate p. 1/33 Modelling Interactions Between Weather and Climate Adam Monahan & Joel Culina & School of Earth and Ocean Sciences, University of Victoria Victoria, BC, Canada
2 Modelling Interactions Between Weather and Climate p. 2/33 Introduction Earth s climate system displays variability on many scales
3 Modelling Interactions Between Weather and Climate p. 2/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m
4 Modelling Interactions Between Weather and Climate p. 2/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds years (and beyond)
5 Modelling Interactions Between Weather and Climate p. 2/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds years (and beyond) Loosely, slower variability called climate and faster called weather
6 Introduction From von Storch and Zwiers, 1999 Modelling Interactions Between Weather and Climate p. 3/33
7 Modelling Interactions Between Weather and Climate p. 4/33 Introduction From von Storch and Zwiers, 1999
8 Modelling Interactions Between Weather and Climate p. 5/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds years (and beyond) Loosely, slower variability called climate and faster called weather Dynamical nonlinearities coupling across space/time scales
9 Modelling Interactions Between Weather and Climate p. 5/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds 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
10 Modelling Interactions Between Weather and Climate p. 5/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds 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
11 Modelling Interactions Between Weather and Climate p. 5/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds 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) suggested stochastic parameterisations more appropriate when separation finite
12 Modelling Interactions Between Weather and Climate p. 5/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds 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) suggested stochastic parameterisations more appropriate when separation finite This talk will consider:
13 Modelling Interactions Between Weather and Climate p. 5/33 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds 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) suggested stochastic parameterisations more appropriate when separation finite This talk will consider: stochastic averaging as a tool for obtaining stochastic climate models from coupled weather/climate system
14 Introduction Earth s climate system displays variability on many scales space: 10 6 m m time: seconds 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) suggested stochastic parameterisations more appropriate when separation finite This talk will consider: stochastic averaging as a tool for obtaining stochastic climate models from coupled weather/climate system two examples: coupled atmosphere/ocean boundary layers, extratropical atmospheric low-frequency variability Modelling Interactions Between Weather and Climate p. 5/33
15 Modelling Interactions Between Weather and Climate p. 6/33 A statistical mechanical perspective Consider a box at temperature T containing N ideal gas molecules
16 Modelling Interactions Between Weather and Climate p. 6/33 A statistical mechanical perspective Consider a box at temperature T containing N ideal gas molecules Kinetic energy of individual molecules randomly distributed with Maxwell-Boltzmann distribution p(e)
17 Modelling Interactions Between Weather and Climate p. 6/33 A statistical mechanical perspective Consider a box at temperature T containing N ideal gas molecules Kinetic energy of individual molecules randomly distributed with Maxwell-Boltzmann distribution p(e) As N mean energy approaches sharp value E = 1 N N E i N E = E p(e) de i=1
18 Modelling Interactions Between Weather and Climate p. 6/33 A statistical mechanical perspective Consider a box at temperature T containing N ideal gas molecules Kinetic energy of individual molecules randomly distributed with Maxwell-Boltzmann distribution p(e) As N mean energy approaches sharp value E = 1 N N E i N E = E p(e) de i=1 For N large (but finite), E is random E = E + 1 N ζ such that (by central limit theorem) ζ is Gaussian
19 Modelling Interactions Between Weather and Climate p. 6/33 A statistical mechanical perspective Consider a box at temperature T containing N ideal gas molecules Kinetic energy of individual molecules randomly distributed with Maxwell-Boltzmann distribution p(e) As N mean energy approaches sharp value E = 1 N N E i N E = E p(e) de i=1 For N large (but finite), E is random E = E + 1 N ζ such that (by central limit theorem) ζ is Gaussian Very large scale separation deterministic dynamics
20 Modelling Interactions Between Weather and Climate p. 6/33 A statistical mechanical perspective Consider a box at temperature T containing N ideal gas molecules Kinetic energy of individual molecules randomly distributed with Maxwell-Boltzmann distribution p(e) As N mean energy approaches sharp value E = 1 N N E i N E = E p(e) de i=1 For N large (but finite), E is random E = E + 1 N ζ such that (by central limit theorem) ζ is Gaussian Very large scale separation deterministic dynamics Smaller (but still large) separation stochastic corrections needed
21 Modelling Interactions Between Weather and Climate p. 7/33 Hasselmann s ansatz: a stochastic climate model Observation: spectra of sea surface temperature (SST) variability are generically red
22 Modelling Interactions Between Weather and Climate p. 7/33 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 & Hasselmann Tellus 1977
23 Modelling Interactions Between Weather and Climate p. 8/33 Hasselmann s ansatz: a stochastic climate model Simple model of Frankignoul & Hasselmann (1977) assumed:
24 Modelling Interactions Between Weather and Climate p. 8/33 Hasselmann s ansatz: a stochastic climate model Simple model of Frankignoul & Hasselmann (1977) assumed: local dynamics of small perturbations T linearised around climatological mean state
25 Modelling Interactions Between Weather and Climate p. 8/33 Hasselmann s ansatz: a stochastic climate model Simple model of Frankignoul & Hasselmann (1977) assumed: local dynamics of small perturbations T linearised around climatological mean state fast atmospheric fluxes represented as white noise
26 Modelling Interactions Between Weather and Climate p. 8/33 Hasselmann s ansatz: a stochastic climate model Simple model of Frankignoul & Hasselmann (1977) assumed: local dynamics of small perturbations T linearised around climatological mean state fast atmospheric fluxes represented as white noise simple Ornstein-Uhlenbeck process with spectrum dt dt = 1 τ T + γẇ E{T (ω) 2 } = γ 2 τ 2 2π(1 + ω 2 τ 2 )
27 Modelling Interactions Between Weather and Climate p. 8/33 Hasselmann s ansatz: a stochastic climate model Simple model of Frankignoul & Hasselmann (1977) assumed: local dynamics of small perturbations T linearised around climatological mean state fast atmospheric fluxes represented as white noise simple Ornstein-Uhlenbeck process with spectrum Linear stochastic model dt dt = 1 τ T + γẇ E{T (ω) 2 } = γ 2 τ 2 2π(1 + ω 2 τ 2 ) simple null hypothesis for observed variability
28 Modelling Interactions Between Weather and Climate p. 8/33 Hasselmann s ansatz: a stochastic climate model Simple model of Frankignoul & Hasselmann (1977) assumed: local dynamics of small perturbations T linearised around climatological mean state fast atmospheric fluxes represented as white noise simple Ornstein-Uhlenbeck process with spectrum Linear stochastic model dt dt = 1 τ T + γẇ E{T (ω) 2 } = γ 2 τ 2 2π(1 + ω 2 τ 2 ) simple null hypothesis for observed variability We ll be coming back to this again later on...
29 Modelling Interactions Between Weather and Climate p. 9/33 Coupled fast-slow systems Starting point for stochastic averaging is assumption that state variable z can be written z = (x,y)
30 Modelling Interactions Between Weather and Climate p. 9/33 Coupled fast-slow systems Starting point for stochastic averaging is assumption that state variable z can be written z = (x,y) where x slow variable (climate) y fast variable (weather) d dt x = f(x,y) d dt y = g(x,y) such that
31 Modelling Interactions Between Weather and Climate p. 9/33 Coupled fast-slow systems Starting point for stochastic averaging is assumption that state variable z can be written z = (x,y) where x slow variable (climate) y fast variable (weather) d dt x = f(x,y) d dt y = g(x,y) such that where is small" τ f(x,y) g(x, y)
32 Modelling Interactions Between Weather and Climate p. 10/33 A simple example As a simple example of a coupled fast-slow system consider d dt x = x x3 + (Σ + x)y d dt y = 1 τ x y + 1 Ẇ τ
33 Modelling Interactions Between Weather and Climate p. 10/33 A simple example As a simple example of a coupled fast-slow system consider d dt x = x x3 + (Σ + x)y d dt y = 1 τ x y + 1 Ẇ τ The weather process y is Gaussian with stationary autocovariance C yy (s) = E {y(t)y(t + s)} = x ( 2 exp s ) τ x as τ 0 τx 2 δ(s)
34 Modelling Interactions Between Weather and Climate p. 10/33 A simple example As a simple example of a coupled fast-slow system consider d dt x = x x3 + (Σ + x)y d dt y = 1 τ x y + 1 Ẇ τ The weather process y is Gaussian with stationary autocovariance C yy (s) = E {y(t)y(t + s)} = x ( 2 exp s ) τ x as τ 0 τx 2 δ(s) Intuition suggests that as τ 0 d dt x x x3 + τ(σ + x) x Ẇ
35 Modelling Interactions Between Weather and Climate p. 11/33 Averaging approximation (A) As τ 0, over finite times we have x(t) x(t)
36 Modelling Interactions Between Weather and Climate p. 11/33 Averaging approximation (A) As τ 0, over finite times we have x(t) x(t) with d dt x = f(x)
37 Modelling Interactions Between Weather and Climate p. 11/33 Averaging approximation (A) As τ 0, over finite times we have x(t) x(t) with where f(x) = d dt x = f(x) f(x,y)dµ x (y) = lim T 1 T T 0 f(x,y(t)) dt
38 Modelling Interactions Between Weather and Climate p. 11/33 Averaging approximation (A) As τ 0, over finite times we have x(t) x(t) with where f(x) = d dt x = f(x) f(x,y)dµ x (y) = lim T 1 T T averages f(x,y) over weather for a fixed climate state 0 f(x,y(t)) dt
39 Modelling Interactions Between Weather and Climate p. 11/33 Averaging approximation (A) As τ 0, over finite times we have x(t) x(t) with where f(x) = d dt x = f(x) f(x,y)dµ x (y) = lim T 1 T T averages f(x,y) over weather for a fixed climate state 0 f(x,y(t)) dt To leading order, climate follows deterministic averaged dynamics
40 Modelling Interactions Between Weather and Climate p. 11/33 Averaging approximation (A) As τ 0, over finite times we have x(t) x(t) with where f(x) = d dt x = f(x) f(x,y)dµ x (y) = lim T 1 T T averages f(x,y) over weather for a fixed climate state 0 f(x,y(t)) dt To leading order, climate follows deterministic averaged dynamics For simple model d dt x = E y x { x x 3 + xy + Σy } = x x 3
41 Modelling Interactions Between Weather and Climate p. 11/33 Averaging approximation (A) As τ 0, over finite times we have x(t) x(t) with d dt x = f(x) where f(x) = f(x,y)dµ x (y) = lim T 1 T T 0 f(x,y(t)) dt averages f(x,y) over weather for a fixed climate state To leading order, climate follows deterministic averaged dynamics For simple model d dt x = E y x { x x 3 + xy + Σy } = x x 3 i.e. depending on x(0) system settles into bottom of one of two potential wells x = ±1
42 Modelling Interactions Between Weather and Climate p. 12/33 Analogy to Reynolds averaging Approximation (A) analogous to Reynolds averaging in fluid mechanics: split flow into mean and turbulent components u = u + u
43 Modelling Interactions Between Weather and Climate p. 12/33 Analogy to Reynolds averaging Approximation (A) analogous to Reynolds averaging in fluid mechanics: split flow into mean and turbulent components u = u + u where u(t) = 1 2T T T u(t + s) ds
44 Modelling Interactions Between Weather and Climate p. 12/33 Analogy to Reynolds averaging Approximation (A) analogous to Reynolds averaging in fluid mechanics: split flow into mean and turbulent components where u(t) = 1 2T u = u + u T T Full flow satisfies Navier-Stokes equations u(t + s) ds t u + u u = force
45 Modelling Interactions Between Weather and Climate p. 12/33 Analogy to Reynolds averaging Approximation (A) analogous to Reynolds averaging in fluid mechanics: split flow into mean and turbulent components where u(t) = 1 2T u = u + u T T Full flow satisfies Navier-Stokes equations u(t + s) ds t u + u u = force mean flow satisfies eddy averaged dynamics t u + u u = force u u
46 Analogy to Reynolds averaging Approximation (A) analogous to Reynolds averaging in fluid mechanics: split flow into mean and turbulent components where u(t) = 1 2T u = u + u T T Full flow satisfies Navier-Stokes equations u(t + s) ds t u + u u = force mean flow satisfies eddy averaged dynamics t u + u u = force u u Note: averaging is projection operator only if scale separation between u and u Modelling Interactions Between Weather and Climate p. 12/33
47 Modelling Interactions Between Weather and Climate p. 13/33 Central limit theorem approximation (L) For τ 0 averaged and full trajectories differ; this difference can be modelled as a random process
48 Modelling Interactions Between Weather and Climate p. 13/33 Central limit theorem approximation (L) For τ 0 averaged and full trajectories differ; this difference can be modelled as a random process Basic result: for τ small x(t) x(t) + ζ
49 Modelling Interactions Between Weather and Climate p. 13/33 Central limit theorem approximation (L) For τ 0 averaged and full trajectories differ; this difference can be modelled as a random process Basic result: for τ small x(t) x(t) + ζ where ζ is the multivariate Ornstein-Uhlenbeck process d ζ = Df(x) ζ + σ(x)ẇ dt
50 Modelling Interactions Between Weather and Climate p. 13/33 Central limit theorem approximation (L) For τ 0 averaged and full trajectories differ; this difference can be modelled as a random process Basic result: for τ small x(t) x(t) + ζ where ζ is the multivariate Ornstein-Uhlenbeck process d ζ = Df(x) ζ + σ(x)ẇ dt Diffusion matrix σ(x) defined by lag correlation integrals σ 2 (x) = E y x {f [x,y(t + s)]f [x,y(t)]} ds where f [x,y(t)] = f[x,y(t)] f[x]
51 Modelling Interactions Between Weather and Climate p. 13/33 Central limit theorem approximation (L) For τ 0 averaged and full trajectories differ; this difference can be modelled as a random process Basic result: for τ small x(t) x(t) + ζ where ζ is the multivariate Ornstein-Uhlenbeck process d ζ = Df(x) ζ + σ(x)ẇ dt Diffusion matrix σ(x) defined by lag correlation integrals σ 2 (x) = E y 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 strict τ 0 limit
52 Modelling Interactions Between Weather and Climate p. 14/33 Central limit theorem approximation (L) Averaging solution x(t) augmented by Gaussian corrections such that x(t) itself not affected
53 Modelling Interactions Between Weather and Climate p. 14/33 Central limit theorem approximation (L) Averaging solution x(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
54 Modelling Interactions Between Weather and Climate p. 14/33 Central limit theorem approximation (L) Averaging solution x(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)
55 Modelling Interactions Between Weather and Climate p. 14/33 Central limit theorem approximation (L) Averaging solution x(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 x 3 + xy + Σy) (x x 3 ) = (x + Σ)y
56 Modelling Interactions Between Weather and Climate p. 14/33 Central limit theorem approximation (L) Averaging solution x(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 x 3 + xy + Σy) (x x 3 ) = (x + Σ)y so σ 2 (x) = (x + Σ) 2 E y x {y(t + s)y(t)} ds = (x + Σ) 2 x 2
57 Central limit theorem approximation (L) Averaging solution x(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 x 3 + xy + Σy) (x x 3 ) = (x + Σ)y so σ 2 (x) = (x + Σ) 2 E y x {y(t + s)y(t)} ds = (x + Σ) 2 x 2 x(t) 1 + ζ such that d dt ζ = 2ζ + (1 + Σ)2 Ẇ Modelling Interactions Between Weather and Climate p. 14/33
58 Modelling Interactions Between Weather and Climate p. 15/33 Hasselmann approximation (N) More complete approximation involves full two-way interaction between weather and climate
59 Modelling Interactions Between Weather and Climate p. 15/33 Hasselmann approximation (N) More complete approximation involves full two-way interaction between weather and climate x(t) solution of stochastic differential equation (SDE) d x = f(x) + σ(x) Ẇ dt
60 Modelling Interactions Between Weather and Climate p. 15/33 Hasselmann approximation (N) More complete approximation involves full two-way interaction between weather and climate x(t) solution of stochastic differential equation (SDE) d x = f(x) + σ(x) Ẇ dt Most accurate of all approximations on long timescales; can capture transitions between metastable states
61 Modelling Interactions Between Weather and Climate p. 15/33 Hasselmann approximation (N) More complete approximation involves full two-way interaction between weather and climate x(t) solution of stochastic differential equation (SDE) d x = f(x) + σ(x) Ẇ dt Most accurate of all approximations on long timescales; can capture transitions between metastable states Noise-induced drift further potential feedback of weather on climate
62 Modelling Interactions Between Weather and Climate p. 15/33 Hasselmann approximation (N) More complete approximation involves full two-way interaction between weather and climate x(t) solution of stochastic differential equation (SDE) d x = f(x) + σ(x) Ẇ dt Most accurate of all approximations on long timescales; can capture transitions between metastable states Noise-induced drift further potential feedback of weather on climate Simple model: d dt x = x x3 + (Σ + x) x Ẇ
63 Modelling Interactions Between Weather and Climate p. 15/33 Hasselmann approximation (N) More complete approximation involves full two-way interaction between weather and climate x(t) solution of stochastic differential equation (SDE) d x = f(x) + σ(x) Ẇ dt Most accurate of all approximations on long timescales; can capture transitions between metastable states Noise-induced drift further potential feedback of weather on climate Simple model: d dt x = x x3 + (Σ + x) x Ẇ Multiplicative noise introduced through:
64 Modelling Interactions Between Weather and Climate p. 15/33 Hasselmann approximation (N) More complete approximation involves full two-way interaction between weather and climate x(t) solution of stochastic differential equation (SDE) d x = f(x) + σ(x) Ẇ dt Most accurate of all approximations on long timescales; can capture transitions between metastable states Noise-induced drift further potential feedback of weather on climate Simple model: d dt x = x x3 + (Σ + x) x Ẇ Multiplicative noise introduced through: nonlinear coupling of x and y in slow dynamics
65 Modelling Interactions Between Weather and Climate p. 15/33 Hasselmann approximation (N) More complete approximation involves full two-way interaction between weather and climate x(t) solution of stochastic differential equation (SDE) d x = f(x) + σ(x) Ẇ dt Most accurate of all approximations on long timescales; can capture transitions between metastable states Noise-induced drift further potential feedback of weather on climate Simple model: d dt x = x x3 + (Σ + x) x Ẇ Multiplicative noise introduced through: nonlinear coupling of x and y in slow dynamics dependence of stationary distribution of y on x
66 Modelling Interactions Between Weather and Climate p. 16/33 When does this work? For the (A) approximation to hold all that is required is a timescale separation
67 Modelling Interactions Between Weather and Climate p. 16/33 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 is strongly mixing
68 Modelling Interactions Between Weather and Climate p. 16/33 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 is strongly mixing Informally, what is required is that the autocorrelation function of y decay sufficiently rapidly with lag for large timescale separations the delta-correlated white noise approximation is reasonable
69 Modelling Interactions Between Weather and Climate p. 16/33 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 is strongly mixing Informally, what is required is that the autocorrelation function of y 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 limit τ 0 as approximation to fast deterministic or stochastic dynamics
70 Modelling Interactions Between Weather and Climate p. 17/33 Relation to MTV Theory" Have assumed that influence of weather on climate is bounded as τ 0
71 Modelling Interactions Between Weather and Climate p. 17/33 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 strengthens in this limit
72 Modelling Interactions Between Weather and Climate p. 17/33 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 strengthens in this limit An example: dx dt dy dt = x + a τ y = 1 x 1 τ τ y + b Ẇ τ
73 Modelling Interactions Between Weather and Climate p. 17/33 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 strengthens in this limit An example: dx dt dy dt = x + a τ y = E y x {y} = τx, std y x = b 2 /2 1 x 1 τ τ y + b Ẇ τ
74 Modelling Interactions Between Weather and Climate p. 17/33 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 strengthens in this limit An example: dx dt dy dt = x + a τ y = E y x {y} = τx, std y x = b 2 /2 1 x 1 τ τ y + b Ẇ τ Stochastic terms remain in the strict τ 0 limit dx dt = (1 a)x + abẇ
75 Modelling Interactions Between Weather and Climate p. 17/33 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 strengthens in this limit An example: dx dt dy dt = x + a τ y = E y x {y} = τx, std y x = b 2 /2 1 x 1 τ τ y + b Ẇ τ Stochastic terms remain in the strict τ 0 limit dx = (1 a)x + abẇ dt Present analysis will not make MTV ansatz of increasing weather influence as τ decreases; rather than a τ = 0 theory", it is a small τ theory"
76 Modelling Interactions Between Weather and Climate p. 18/33 Case study 1: Coupled atmosphere-ocean boundary layers Atmosphere and ocean interact through (generally turbulent) boundary layers, exchanging mass, momentum, and energy
77 Modelling Interactions Between Weather and Climate p. 18/33 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 dt u = Π u c d(w) h(t a, T o ) wu d dt v = c d(w) h(t a, T o ) wv d w e h(t a, T o ) u + σ uẇ1 w e h(t a, T o ) v + σ uẇ2 dt T a = β (T o T a )w β θ(w µ w ) λ a T a + Σ 11 Ẇ 3 + Σ 12 Ẇ 4 γ a γ a γ a d dt T o = β (T a T o )w + β θ(w µ w ) λ o T o + Σ 21 Ẇ 3 + Σ 22 Ẇ 4 γ o γ o γ o
78 Modelling Interactions Between Weather and Climate p. 19/33 Case study 1: Coupled atmosphere-ocean boundary layers 60 o N 54 o N 48 o N OSP 42 o N 36 o N 30 o N 165 o E 180 o W 165 o W 150 o W 135 o W 120 o W Ocean station P
79 Modelling Interactions Between Weather and Climate p. 20/33 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 dt u = Π u c d(w) h(t a, T o ) wu d dt v = c d(w) h(t a, T o ) wv d w e h(t a, T o ) u + σ uẇ1 w e h(t a, T o ) v + σ uẇ2 dt T a = β (T o T a )w β θ(w µ w ) λ a T a + Σ 11 Ẇ 3 + Σ 12 Ẇ 4 γ a γ a γ a d dt T o = β (T a T o )w + β θ(w µ w ) λ o T o + Σ 21 Ẇ 3 + Σ 22 Ẇ 4 γ o γ o γ o Atmospheric boundary layer thickness determined by surface stratification h(t a, T o ) = max [ h min, h(1 α(t a T o )) ]
80 Modelling Interactions Between Weather and Climate p. 21/33 Case study 1: Coupled atmosphere-ocean boundary layers h (m) T a T o (K) Boundary layer height and surface stability
81 Case study 1: Coupled atmosphere-ocean boundary layers correlation lag (days) T a (obs) T a (sim) T o (obs) T o (sim) T o (K) T a (K) Obs Sim 0.8 Obs Sim Obs Sim correlation p(w) lag (days) w (m/s) Observed and simulated statistics (Ocean Station P) Modelling Interactions Between Weather and Climate p. 22/33
82 Modelling Interactions Between Weather and Climate p. 23/33 Case study 1: Coupled atmosphere-ocean boundary layers Observed timescale separations: τ w τ Ta 0.7
83 Modelling Interactions Between Weather and Climate p. 23/33 Case study 1: Coupled atmosphere-ocean boundary layers Observed timescale separations: τ w τ Ta 0.7 Take x = T = (T a,t o ), y = (u,v) with
84 Case study 1: Coupled atmosphere-ocean boundary layers Observed timescale separations: τ w τ Ta 0.7 Take x = T = (T a,t o ), y = (u,v) with Averaging model (A) 5 f 1 ( K / s ) d T = f(t) + ΣẆ dt 5 f 2 ( K / s ) T o (K) T a (K) T a (K) x x 10 6 Modelling Interactions Between Weather and Climate p. 23/33
85 Modelling Interactions Between Weather and Climate p. 24/33 Case study 1: Coupled atmosphere-ocean boundary layers Analytic expression for σ(t a, T o ) σ(t a, T o ) = β γ 2 a + γ 2 o γ o γ a 1 1 γ a γ o ψ(t a T o )
86 Modelling Interactions Between Weather and Climate p. 24/33 Case study 1: Coupled atmosphere-ocean boundary layers Analytic expression for σ(t a, T o ) σ(t a, T o ) = β γ 2 a + γ 2 o γ o γ a 1 1 γ a γ o ψ(t a T o ) where ψ(t a T o ) = (T a T o + θ) E {w (t + s)w (t)} ds
87 Case study 1: Coupled atmosphere-ocean boundary layers Analytic expression for σ(t a, T o ) σ(t a, T o ) = β γ 2 a + γ 2 o γ o γ a 1 1 γ a γ o ψ(t a T o ) where ψ(t a T o ) = (T a T o + θ) E {w (t + s)w (t)} ds 0.5 ψ (K m s 1/ ) T a T o (K) 5 10 Modelling Interactions Between Weather and Climate p. 24/33
88 Modelling Interactions Between Weather and Climate p. 25/33 Case study 1: Coupled atmosphere-ocean boundary layers correlation correlation lag (days) T (reduced) a T (full) a T o (reduced) T (full) o lag (days) T (reduced) a T (full) a T o (reduced) T (full) o T o (K) T o (K) T a (K) T a (K) Reduced Full Reduced Full τ = 0.7 τ = 0.35
89 Modelling Interactions Between Weather and Climate p. 26/33 Case study 1: Coupled atmosphere-ocean boundary layers Sura and Newman (2008) fit observations at OSP to linear multiplicative SDE d T = AT + B(T) Ẇ dt
90 Modelling Interactions Between Weather and Climate p. 26/33 Case study 1: Coupled atmosphere-ocean boundary layers Sura and Newman (2008) fit observations at OSP to linear multiplicative SDE d T = AT + B(T) Ẇ dt Form of this SDE justified through simple substitution in full coupled dynamics w µ w + σẇ
91 Modelling Interactions Between Weather and Climate p. 26/33 Case study 1: Coupled atmosphere-ocean boundary layers Sura and Newman (2008) fit observations at OSP to linear multiplicative SDE d T = AT + B(T) Ẇ dt Form of this SDE justified through simple substitution in full coupled dynamics w µ w + σẇ Form of SDE did not account for feedback of stratification on h; physical origin of parameters in inverse model somewhat different than had been assumed
92 Modelling Interactions Between Weather and Climate p. 27/33 Case study 2: extratropical atmospheric LFV Variability of extratropical atmosphere on timescales of 10 days + descrbed as low-frequency variability (LFV)
93 Modelling Interactions Between Weather and Climate p. 27/33 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
94 Modelling Interactions Between Weather and Climate p. 27/33 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) From
95 Modelling Interactions Between Weather and Climate p. 27/33 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) Basic structures characterised statistically; dynamical questions remain open From
96 Modelling Interactions Between Weather and Climate p. 27/33 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) Basic structures characterised statistically; dynamical questions remain open Empirical stochastic models have been useful for studying LFV From
97 Modelling Interactions Between Weather and Climate p. 27/33 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) From 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
98 Case study 2: extratropical atmospheric LFV Kravtsov et al. JAS 2005 Modelling Interactions Between Weather and Climate p. 28/33
99 Modelling Interactions Between Weather and Climate p. 29/33 Case study 2: extratropical atmospheric LFV Using PCA decomposition, identify 2 slow modes: propagating wavenumber 4, stationary zonal
100 Modelling Interactions Between Weather and Climate p. 29/33 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
101 Modelling Interactions Between Weather and Climate p. 29/33 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 (stationary mode) models
102 Case study 2: extratropical atmospheric LFV Modelling Interactions Between Weather and Climate p. 30/33
103 Case study 2: extratropical atmospheric LFV Modelling Interactions Between Weather and Climate p. 31/33
104 Modelling Interactions Between Weather and Climate p. 31/33 Case study 2: extratropical atmospheric LFV Stationary mode potential shows prominent shoulder
105 Modelling Interactions Between Weather and Climate p. 31/33 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
106 Modelling Interactions Between Weather and Climate p. 31/33 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
107 Modelling Interactions Between Weather and Climate p. 32/33 Case study 2: extratropical atmospheric LFV MTV approximations with minimal regression tuning
108 Modelling Interactions Between Weather and Climate p. 33/33 Conclusions Stochastic reduction tools allow systematic derivation of low-order climate models from coupled weather/climate systems
109 Modelling Interactions Between Weather and Climate p. 33/33 Conclusions Stochastic reduction tools allow systematic derivation of low-order climate models from coupled weather/climate systems Reduction approach is straightforward, although implementation in high-dimensional systems challenging
110 Modelling Interactions Between Weather and Climate p. 33/33 Conclusions Stochastic reduction tools allow systematic derivation of low-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
111 Modelling Interactions Between Weather and Climate p. 33/33 Conclusions Stochastic reduction tools allow systematic derivation of low-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 benefit of less restrictive assumptions
112 Modelling Interactions Between Weather and Climate p. 33/33 Conclusions Stochastic reduction tools allow systematic derivation of low-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 benefit of less restrictive assumptions drawbacks of more difficult implementation, lack of closed-form solution
113 Modelling Interactions Between Weather and Climate p. 33/33 Conclusions Stochastic reduction tools allow systematic derivation of low-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 benefit of less restrictive assumptions drawbacks of 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
Stochastic differential equation models in biology Susanne Ditlevsen
Stochastic differential equation models in biology Susanne Ditlevsen Introduction This chapter is concerned with continuous time processes, which are often modeled as a system of ordinary differential
More informationEmpirical climate models of coupled tropical atmosphere-ocean dynamics!
Empirical climate models of coupled tropical atmosphere-ocean dynamics! Matt Newman CIRES/CDC and NOAA/ESRL/PSD Work done by Prashant Sardeshmukh, Cécile Penland, Mike Alexander, Jamie Scott, and me Outline
More informationIntroduction to asymptotic techniques for stochastic systems with multiple time-scales
Introduction to asymptotic techniques for stochastic systems with multiple time-scales Eric Vanden-Eijnden Courant Institute Motivating examples Consider the ODE {Ẋ = Y 3 + sin(πt) + cos( 2πt) X() = x
More informationClimate Change and Variability in the Southern Hemisphere: An Atmospheric Dynamics Perspective
Climate Change and Variability in the Southern Hemisphere: An Atmospheric Dynamics Perspective Edwin P. Gerber Center for Atmosphere Ocean Science Courant Institute of Mathematical Sciences New York University
More informationInterest Rate Models:
1/17 Interest Rate Models: from Parametric Statistics to Infinite Dimensional Stochastic Analysis René Carmona Bendheim Center for Finance ORFE & PACM, Princeton University email: rcarmna@princeton.edu
More informationThe concentration of a drug in blood. Exponential decay. Different realizations. Exponential decay with noise. dc(t) dt.
The concentration of a drug in blood Exponential decay C12 concentration 2 4 6 8 1 C12 concentration 2 4 6 8 1 dc(t) dt = µc(t) C(t) = C()e µt 2 4 6 8 1 12 time in minutes 2 4 6 8 1 12 time in minutes
More informationThe impacts of stochastic noise on climate models
The impacts of stochastic noise on climate models Paul Williams Department of Meteorology, University of Reading, UK The impacts of στοχαστικός noise on climate models Paul Williams Department of Meteorology,
More informationEstimation of State Noise for the Ensemble Kalman filter algorithm for 2D shallow water equations.
Estimation of State Noise for the Ensemble Kalman filter algorithm for 2D shallow water equations. May 6, 2009 Motivation Constitutive Equations EnKF algorithm Some results Method Navier Stokes equations
More informationNote the diverse scales of eddy motion and self-similar appearance at different lengthscales of the turbulence in this water jet. Only eddies of size
L Note the diverse scales of eddy motion and self-similar appearance at different lengthscales of the turbulence in this water jet. Only eddies of size 0.01L or smaller are subject to substantial viscous
More informationStochastic Processes. A stochastic process is a function of two variables:
Stochastic Processes Stochastic: from Greek stochastikos, proceeding by guesswork, literally, skillful in aiming. A stochastic process is simply a collection of random variables labelled by some parameter:
More informationIntroduction to multiscale modeling and simulation. Explicit methods for ODEs : forward Euler. y n+1 = y n + tf(y n ) dy dt = f(y), y(0) = y 0
Introduction to multiscale modeling and simulation Lecture 5 Numerical methods for ODEs, SDEs and PDEs The need for multiscale methods Two generic frameworks for multiscale computation Explicit methods
More informationAn Introduction to Nonlinear Principal Component Analysis
An Introduction tononlinearprincipal Component Analysis p. 1/33 An Introduction to Nonlinear Principal Component Analysis Adam Monahan monahana@uvic.ca School of Earth and Ocean Sciences University of
More informationLecture 2. Turbulent Flow
Lecture 2. Turbulent Flow Note the diverse scales of eddy motion and self-similar appearance at different lengthscales of this turbulent water jet. If L is the size of the largest eddies, only very small
More informationI forgot to mention last time: in the Ito formula for two standard processes, putting
I forgot to mention last time: in the Ito formula for two standard processes, putting dx t = a t dt + b t db t dy t = α t dt + β t db t, and taking f(x, y = xy, one has f x = y, f y = x, and f xx = f yy
More informationTurbulent Flows. g u
.4 g u.3.2.1 t. 6 4 2 2 4 6 Figure 12.1: Effect of diffusion on PDF shape: solution to Eq. (12.29) for Dt =,.2,.2, 1. The dashed line is the Gaussian with the same mean () and variance (3) as the PDF at
More informationESCI 485 Air/Sea Interaction Lesson 1 Stresses and Fluxes Dr. DeCaria
ESCI 485 Air/Sea Interaction Lesson 1 Stresses and Fluxes Dr DeCaria References: An Introduction to Dynamic Meteorology, Holton MOMENTUM EQUATIONS The momentum equations governing the ocean or atmosphere
More informationStochastic contraction BACS Workshop Chamonix, January 14, 2008
Stochastic contraction BACS Workshop Chamonix, January 14, 2008 Q.-C. Pham N. Tabareau J.-J. Slotine Q.-C. Pham, N. Tabareau, J.-J. Slotine () Stochastic contraction 1 / 19 Why stochastic contraction?
More informationWeek 9 Generators, duality, change of measure
Week 9 Generators, duality, change of measure Jonathan Goodman November 18, 013 1 Generators This section describes a common abstract way to describe many of the differential equations related to Markov
More informationGaussian Process Approximations of Stochastic Differential Equations
Gaussian Process Approximations of Stochastic Differential Equations Cédric Archambeau Dan Cawford Manfred Opper John Shawe-Taylor May, 2006 1 Introduction Some of the most complex models routinely run
More information16.584: Random (Stochastic) Processes
1 16.584: Random (Stochastic) Processes X(t): X : RV : Continuous function of the independent variable t (time, space etc.) Random process : Collection of X(t, ζ) : Indexed on another independent variable
More informationComplex system approach to geospace and climate studies. Tatjana Živković
Complex system approach to geospace and climate studies Tatjana Živković 30.11.2011 Outline of a talk Importance of complex system approach Phase space reconstruction Recurrence plot analysis Test for
More informationBred Vectors: A simple tool to understand complex dynamics
Bred Vectors: A simple tool to understand complex dynamics With deep gratitude to Akio Arakawa for all the understanding he has given us Eugenia Kalnay, Shu-Chih Yang, Malaquías Peña, Ming Cai and Matt
More informationLarge Deviations for Small-Noise Stochastic Differential Equations
Chapter 21 Large Deviations for Small-Noise Stochastic Differential Equations This lecture is at once the end of our main consideration of diffusions and stochastic calculus, and a first taste of large
More informationLarge Deviations for Small-Noise Stochastic Differential Equations
Chapter 22 Large Deviations for Small-Noise Stochastic Differential Equations This lecture is at once the end of our main consideration of diffusions and stochastic calculus, and a first taste of large
More informationEffects of Forcing Scheme on the Flow and the Relative Motion of Inertial Particles in DNS of Isotropic Turbulence
Effects of Forcing Scheme on the Flow and the Relative Motion of Inertial Particles in DNS of Isotropic Turbulence Rohit Dhariwal and Vijaya Rani PI: Sarma L. Rani Department of Mechanical and Aerospace
More informationModel Specification Testing in Nonparametric and Semiparametric Time Series Econometrics. Jiti Gao
Model Specification Testing in Nonparametric and Semiparametric Time Series Econometrics Jiti Gao Department of Statistics School of Mathematics and Statistics The University of Western Australia Crawley
More informationSensitivity of an Ocean Model to Details of Stochastic Forcing
Sensitivity of an Ocean Model to Details of Stochastic Forcing Cécile Penland and Philip Sura NOAA-CIRES/Climate Diagnostics Center, Boulder CO 80305-3328 Abstract We review the sensitivity of a double-gyre
More informationThe global ocean circulation: an elegant dynamical system
The global ocean circulation: an elegant dynamical system Henk Dijkstra Institute for Marine and Atmospheric research Utrecht (IMAU), Department of Physics and Astronomy, Utrecht University, The Netherlands
More informationHomogenization with stochastic differential equations
Homogenization with stochastic differential equations Scott Hottovy shottovy@math.arizona.edu University of Arizona Program in Applied Mathematics October 12, 2011 Modeling with SDE Use SDE to model system
More informationGaussian processes for inference in stochastic differential equations
Gaussian processes for inference in stochastic differential equations Manfred Opper, AI group, TU Berlin November 6, 2017 Manfred Opper, AI group, TU Berlin (TU Berlin) inference in SDE November 6, 2017
More informationHandbook of Stochastic Methods
C. W. Gardiner Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences Third Edition With 30 Figures Springer Contents 1. A Historical Introduction 1 1.1 Motivation I 1.2 Some Historical
More information3. Midlatitude Storm Tracks and the North Atlantic Oscillation
3. Midlatitude Storm Tracks and the North Atlantic Oscillation Copyright 2006 Emily Shuckburgh, University of Cambridge. Not to be quoted or reproduced without permission. EFS 3/1 Review of key results
More informationHow does the stratosphere influence the troposphere in mechanistic GCMs?
How does the stratosphere influence the troposphere in mechanistic GCMs? Walt Robinson Department of Atmospheric Sciences University of Illinois at Urbana-Champaign Acknowledgments: Dr. Yucheng Song, Climate
More informationThe Role of Ocean Dynamics in North Atlantic Tripole Variability
The Role of Ocean Dynamics in North Atlantic Tripole Variability 1951-2000 Edwin K. Schneider George Mason University and COLA C20C Workshop, Beijing October 2010 Collaborators Meizhu Fan - many of the
More informationLANGEVIN THEORY OF BROWNIAN MOTION. Contents. 1 Langevin theory. 1 Langevin theory 1. 2 The Ornstein-Uhlenbeck process 8
Contents LANGEVIN THEORY OF BROWNIAN MOTION 1 Langevin theory 1 2 The Ornstein-Uhlenbeck process 8 1 Langevin theory Einstein (as well as Smoluchowski) was well aware that the theory of Brownian motion
More informationOn the (multi)scale nature of fluid turbulence
On the (multi)scale nature of fluid turbulence Kolmogorov axiomatics Laurent Chevillard Laboratoire de Physique, ENS Lyon, CNRS, France Laurent Chevillard, Laboratoire de Physique, ENS Lyon, CNRS, France
More informationBred vectors: theory and applications in operational forecasting. Eugenia Kalnay Lecture 3 Alghero, May 2008
Bred vectors: theory and applications in operational forecasting. Eugenia Kalnay Lecture 3 Alghero, May 2008 ca. 1974 Central theorem of chaos (Lorenz, 1960s): a) Unstable systems have finite predictability
More informationPROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS
PROBABILITY: LIMIT THEOREMS II, SPRING 218. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please
More information1 Brownian Local Time
1 Brownian Local Time We first begin by defining the space and variables for Brownian local time. Let W t be a standard 1-D Wiener process. We know that for the set, {t : W t = } P (µ{t : W t = } = ) =
More informationOn Stochastic Adaptive Control & its Applications. Bozenna Pasik-Duncan University of Kansas, USA
On Stochastic Adaptive Control & its Applications Bozenna Pasik-Duncan University of Kansas, USA ASEAS Workshop, AFOSR, 23-24 March, 2009 1. Motivation: Work in the 1970's 2. Adaptive Control of Continuous
More informationStochastic stability of open-ocean deep convection
Stochastic stability of open-ocean deep convection Till Kuhlbrodt kuhlbrodt@pik-potsdam.de Institute of Physics, University of Potsdam Am Neuen Palais 1, 14469 Potsdam, Germany and Potsdam Institute for
More informationSDE Coefficients. March 4, 2008
SDE Coefficients March 4, 2008 The following is a summary of GARD sections 3.3 and 6., mainly as an overview of the two main approaches to creating a SDE model. Stochastic Differential Equations (SDE)
More informationLecture 1: Pragmatic Introduction to Stochastic Differential Equations
Lecture 1: Pragmatic Introduction to Stochastic Differential Equations Simo Särkkä Aalto University, Finland (visiting at Oxford University, UK) November 13, 2013 Simo Särkkä (Aalto) Lecture 1: Pragmatic
More informationStratospheric Influence on Polar Climate. Paul Kushner, Dept. of Physics, University of Toronto
Stratospheric Influence on Polar Climate Paul Kushner, Dept. of Physics, University of Toronto Aims for Today 1. Discuss different types of stratospheric influence. 2. Describe some dynamical aspects.
More informationRandom and Deterministic perturbations of dynamical systems. Leonid Koralov
Random and Deterministic perturbations of dynamical systems Leonid Koralov - M. Freidlin, L. Koralov Metastability for Nonlinear Random Perturbations of Dynamical Systems, Stochastic Processes and Applications
More informationGFD 2012 Lecture 1: Dynamics of Coherent Structures and their Impact on Transport and Predictability
GFD 2012 Lecture 1: Dynamics of Coherent Structures and their Impact on Transport and Predictability Jeffrey B. Weiss; notes by Duncan Hewitt and Pedram Hassanzadeh June 18, 2012 1 Introduction 1.1 What
More informationPreferred spatio-temporal patterns as non-equilibrium currents
Preferred spatio-temporal patterns as non-equilibrium currents Escher Jeffrey B. Weiss Atmospheric and Oceanic Sciences University of Colorado, Boulder Arin Nelson, CU Baylor Fox-Kemper, Brown U Royce
More informationAn eddifying Stommel model: Fast eddy effects in a two-box ocean arxiv: v2 [math.ds] 5 Oct 2017
An eddifying Stommel model: Fast eddy effects in a two-box ocean arxiv:705.06706v2 [math.ds] 5 Oct 207 William Barham and Ian Grooms Department of Applied Mathematics, University of Colorado, Boulder,
More informationwhere r n = dn+1 x(t)
Random Variables Overview Probability Random variables Transforms of pdfs Moments and cumulants Useful distributions Random vectors Linear transformations of random vectors The multivariate normal distribution
More informationDifferent approaches to model wind speed based on stochastic differential equations
1 Universidad de Castilla La Mancha Different approaches to model wind speed based on stochastic differential equations Rafael Zárate-Miñano Escuela de Ingeniería Minera e Industrial de Almadén Universidad
More informationA scaling limit from Euler to Navier-Stokes equations with random perturbation
A scaling limit from Euler to Navier-Stokes equations with random perturbation Franco Flandoli, Scuola Normale Superiore of Pisa Newton Institute, October 208 Newton Institute, October 208 / Subject of
More informationHarnack Inequalities and Applications for Stochastic Equations
p. 1/32 Harnack Inequalities and Applications for Stochastic Equations PhD Thesis Defense Shun-Xiang Ouyang Under the Supervision of Prof. Michael Röckner & Prof. Feng-Yu Wang March 6, 29 p. 2/32 Outline
More informationSmoluchowski Diffusion Equation
Chapter 4 Smoluchowski Diffusion Equation Contents 4. Derivation of the Smoluchoswki Diffusion Equation for Potential Fields 64 4.2 One-DimensionalDiffusoninaLinearPotential... 67 4.2. Diffusion in an
More informationELEG 3143 Probability & Stochastic Process Ch. 6 Stochastic Process
Department of Electrical Engineering University of Arkansas ELEG 3143 Probability & Stochastic Process Ch. 6 Stochastic Process Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Definition of stochastic process (random
More informationThis is a Gaussian probability centered around m = 0 (the most probable and mean position is the origin) and the mean square displacement m 2 = n,or
Physics 7b: Statistical Mechanics Brownian Motion Brownian motion is the motion of a particle due to the buffeting by the molecules in a gas or liquid. The particle must be small enough that the effects
More informationStochastic Gradient Descent in Continuous Time
Stochastic Gradient Descent in Continuous Time Justin Sirignano University of Illinois at Urbana Champaign with Konstantinos Spiliopoulos (Boston University) 1 / 27 We consider a diffusion X t X = R m
More informationStochastic Volatility and Correction to the Heat Equation
Stochastic Volatility and Correction to the Heat Equation Jean-Pierre Fouque, George Papanicolaou and Ronnie Sircar Abstract. From a probabilist s point of view the Twentieth Century has been a century
More informationDynamical systems with Gaussian and Levy noise: analytical and stochastic approaches
Dynamical systems with Gaussian and Levy noise: analytical and stochastic approaches Noise is often considered as some disturbing component of the system. In particular physical situations, noise becomes
More informationSimple Mathematical, Dynamical Stochastic Models Capturing the Observed Diversity of the El Niño Southern Oscillation (ENSO)
Simple Mathematical, Dynamical Stochastic Models Capturing the Observed Diversity of the El Niño Southern Oscillation (ENSO) Lecture 5: A Simple Stochastic Model for El Niño with Westerly Wind Bursts Andrew
More informationEnergy Barriers and Rates - Transition State Theory for Physicists
Energy Barriers and Rates - Transition State Theory for Physicists Daniel C. Elton October 12, 2013 Useful relations 1 cal = 4.184 J 1 kcal mole 1 = 0.0434 ev per particle 1 kj mole 1 = 0.0104 ev per particle
More information1 Elementary probability
1 Elementary probability Problem 1.1 (*) A coin is thrown several times. Find the probability, that at the n-th experiment: (a) Head appears for the first time (b) Head and Tail have appeared equal number
More informationEffects of Forcing Scheme on the Flow and the Relative Motion of Inertial Particles in DNS of Isotropic Turbulence
Effects of Forcing Scheme on the Flow and the Relative Motion of Inertial Particles in DNS of Isotropic Turbulence Rohit Dhariwal PI: Sarma L. Rani Department of Mechanical and Aerospace Engineering The
More informationThe Kramers problem and first passage times.
Chapter 8 The Kramers problem and first passage times. The Kramers problem is to find the rate at which a Brownian particle escapes from a potential well over a potential barrier. One method of attack
More informationLAN property for sde s with additive fractional noise and continuous time observation
LAN property for sde s with additive fractional noise and continuous time observation Eulalia Nualart (Universitat Pompeu Fabra, Barcelona) joint work with Samy Tindel (Purdue University) Vlad s 6th birthday,
More informationLecture 1: Introduction to Linear and Non-Linear Waves
Lecture 1: Introduction to Linear and Non-Linear Waves Lecturer: Harvey Segur. Write-up: Michael Bates June 15, 2009 1 Introduction to Water Waves 1.1 Motivation and Basic Properties There are many types
More informationBill Scheftic Feb 2nd 2008 Atmo595c
Bill Scheftic Feb 2nd 2008 Atmo595c Review: White Noise and Red Noise ` ` Hasselman s noise forced climate External Climate forcings: Insolation Cycles Mechanisms for continuous climate variability Stochastic/Coherence
More informationLETTERS. North Atlantic Atmosphere Ocean Interaction on Intraseasonal Time Scales
VOL. 17, NO. 8 JOURNAL OF CLIMATE 15 APRIL 2004 LETTERS North Atlantic Atmosphere Ocean Interaction on Intraseasonal Time Scales LAURA M. CIASTO AND DAVID W. J. THOMPSON Department of Atmospheric Science,
More informationStochastic continuity equation and related processes
Stochastic continuity equation and related processes Gabriele Bassi c Armando Bazzani a Helmut Mais b Giorgio Turchetti a a Dept. of Physics Univ. of Bologna, INFN Sezione di Bologna, ITALY b DESY, Hamburg,
More informationNORTH ATLANTIC DECADAL-TO- MULTIDECADAL VARIABILITY - MECHANISMS AND PREDICTABILITY
NORTH ATLANTIC DECADAL-TO- MULTIDECADAL VARIABILITY - MECHANISMS AND PREDICTABILITY Noel Keenlyside Geophysical Institute, University of Bergen Jin Ba, Jennifer Mecking, and Nour-Eddine Omrani NTU International
More informationp 1 ( Y p dp) 1/p ( X p dp) 1 1 p
Doob s inequality Let X(t) be a right continuous submartingale with respect to F(t), t 1 P(sup s t X(s) λ) 1 λ {sup s t X(s) λ} X + (t)dp 2 For 1 < p
More informationBernardo D Auria Stochastic Processes /10. Notes. Abril 13 th, 2010
1 Stochastic Calculus Notes Abril 13 th, 1 As we have seen in previous lessons, the stochastic integral with respect to the Brownian motion shows a behavior different from the classical Riemann-Stieltjes
More informationStochastic differential equations in neuroscience
Stochastic differential equations in neuroscience Nils Berglund MAPMO, Orléans (CNRS, UMR 6628) http://www.univ-orleans.fr/mapmo/membres/berglund/ Barbara Gentz, Universität Bielefeld Damien Landon, MAPMO-Orléans
More information5 Applying the Fokker-Planck equation
5 Applying the Fokker-Planck equation We begin with one-dimensional examples, keeping g = constant. Recall: the FPE for the Langevin equation with η(t 1 )η(t ) = κδ(t 1 t ) is = f(x) + g(x)η(t) t = x [f(x)p
More information14 - Gaussian Stochastic Processes
14-1 Gaussian Stochastic Processes S. Lall, Stanford 211.2.24.1 14 - Gaussian Stochastic Processes Linear systems driven by IID noise Evolution of mean and covariance Example: mass-spring system Steady-state
More informationECE 636: Systems identification
ECE 636: Systems identification Lectures 3 4 Random variables/signals (continued) Random/stochastic vectors Random signals and linear systems Random signals in the frequency domain υ ε x S z + y Experimental
More informationMortality Surface by Means of Continuous Time Cohort Models
Mortality Surface by Means of Continuous Time Cohort Models Petar Jevtić, Elisa Luciano and Elena Vigna Longevity Eight 2012, Waterloo, Canada, 7-8 September 2012 Outline 1 Introduction Model construction
More informationHandbook of Stochastic Methods
Springer Series in Synergetics 13 Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences von Crispin W Gardiner Neuausgabe Handbook of Stochastic Methods Gardiner schnell und portofrei
More informationLocal vs. Nonlocal Diffusions A Tale of Two Laplacians
Local vs. Nonlocal Diffusions A Tale of Two Laplacians Jinqiao Duan Dept of Applied Mathematics Illinois Institute of Technology Chicago duan@iit.edu Outline 1 Einstein & Wiener: The Local diffusion 2
More informationThe Probability Distribution of Sea Surface Wind Speeds: Effects of Variable Surface Stratification and Boundary Layer Thickness
The Probability Distribution of Sea Surface Wind Speeds: Effects of Variable Surface Stratification and Boundary Layer Thickness Adam Hugh Monahan (monahana@uvic.ca) School of Earth and Ocean Sciences
More informationEAS 305 Random Processes Viewgraph 1 of 10. Random Processes
EAS 305 Random Processes Viewgraph 1 of 10 Definitions: Random Processes A random process is a family of random variables indexed by a parameter t T, where T is called the index set λ i Experiment outcome
More informationThe Euler Equation of Gas-Dynamics
The Euler Equation of Gas-Dynamics A. Mignone October 24, 217 In this lecture we study some properties of the Euler equations of gasdynamics, + (u) = ( ) u + u u + p = a p + u p + γp u = where, p and u
More informationThe Evolution of Large-Amplitude Internal Gravity Wavepackets
The Evolution of Large-Amplitude Internal Gravity Wavepackets Sutherland, Bruce R. and Brown, Geoffrey L. University of Alberta Environmental and Industrial Fluid Dynamics Laboratory Edmonton, Alberta,
More informationRegularization by noise in infinite dimensions
Regularization by noise in infinite dimensions Franco Flandoli, University of Pisa King s College 2017 Franco Flandoli, University of Pisa () Regularization by noise King s College 2017 1 / 33 Plan of
More informationSnapshots of sea-level pressure are dominated in mid-latitude regions by synoptic-scale features known as cyclones (SLP minima) and anticyclones (SLP
1 Snapshots of sea-level pressure are dominated in mid-latitude regions by synoptic-scale features known as cyclones (SLP minima) and anticyclones (SLP maxima). These features have lifetimes on the order
More informationα 2 )(k x ũ(t, η) + k z W (t, η)
1 3D Poiseuille Flow Over the next two lectures we will be going over the stabilization of the 3-D Poiseuille flow. For those of you who havent had fluids, that means we have a channel of fluid that is
More informationENSC327 Communications Systems 19: Random Processes. Jie Liang School of Engineering Science Simon Fraser University
ENSC327 Communications Systems 19: Random Processes Jie Liang School of Engineering Science Simon Fraser University 1 Outline Random processes Stationary random processes Autocorrelation of random processes
More informationIntroduction to Relaxation Theory James Keeler
EUROMAR Zürich, 24 Introduction to Relaxation Theory James Keeler University of Cambridge Department of Chemistry What is relaxation? Why might it be interesting? relaxation is the process which drives
More informationThird In-Class Exam Solutions Math 246, Professor David Levermore Thursday, 3 December 2009 (1) [6] Given that 2 is an eigenvalue of the matrix
Third In-Class Exam Solutions Math 26, Professor David Levermore Thursday, December 2009 ) [6] Given that 2 is an eigenvalue of the matrix A 2, 0 find all the eigenvectors of A associated with 2. Solution.
More informationA State Space Model for Wind Forecast Correction
A State Space Model for Wind Forecast Correction Valrie Monbe, Pierre Ailliot 2, and Anne Cuzol 1 1 Lab-STICC, Université Européenne de Bretagne, France (e-mail: valerie.monbet@univ-ubs.fr, anne.cuzol@univ-ubs.fr)
More informationGlobal well-posedness of the primitive equations of oceanic and atmospheric dynamics
Global well-posedness of the primitive equations of oceanic and atmospheric dynamics Jinkai Li Department of Mathematics The Chinese University of Hong Kong Dynamics of Small Scales in Fluids ICERM, Feb
More information2. (a) What is gaussian random variable? Develop an equation for guassian distribution
Code No: R059210401 Set No. 1 II B.Tech I Semester Supplementary Examinations, February 2007 PROBABILITY THEORY AND STOCHASTIC PROCESS ( Common to Electronics & Communication Engineering, Electronics &
More informationOn continuous time contract theory
Ecole Polytechnique, France Journée de rentrée du CMAP, 3 octobre, 218 Outline 1 2 Semimartingale measures on the canonical space Random horizon 2nd order backward SDEs (Static) Principal-Agent Problem
More informationPredictability & Variability of the Ocean Circulation: A non-normal perspective
Predictability & Variability of the Ocean Circulation: A non-normal perspective Laure Zanna University of Oxford Eli Tziperman (Harvard University) Patrick Heimbach (MIT) Andrew M. Moore (UC Santa Cruz)
More informationStochastic Stokes drift of a flexible dumbbell
Stochastic Stokes drift of a flexible dumbbell Kalvis M. Jansons arxiv:math/0607797v4 [math.pr] 22 Mar 2007 Department of Mathematics, University College London, Gower Street, London WC1E 6BT, UK January
More informationROSSBY WAVE PROPAGATION
ROSSBY WAVE PROPAGATION (PHH lecture 4) The presence of a gradient of PV (or q.-g. p.v.) allows slow wave motions generally called Rossby waves These waves arise through the Rossby restoration mechanism,
More informationTheoretical Tutorial Session 2
1 / 36 Theoretical Tutorial Session 2 Xiaoming Song Department of Mathematics Drexel University July 27, 216 Outline 2 / 36 Itô s formula Martingale representation theorem Stochastic differential equations
More information{σ x >t}p x. (σ x >t)=e at.
3.11. EXERCISES 121 3.11 Exercises Exercise 3.1 Consider the Ornstein Uhlenbeck process in example 3.1.7(B). Show that the defined process is a Markov process which converges in distribution to an N(0,σ
More informationExercises. T 2T. e ita φ(t)dt.
Exercises. Set #. Construct an example of a sequence of probability measures P n on R which converge weakly to a probability measure P but so that the first moments m,n = xdp n do not converge to m = xdp.
More informationHierarchy of Models and Model Reduction in Climate Dynamics
INRIA-CEA-EDF School on Model Reduction, 8 11 Oct. 2007 Hierarchy of Models and Model Reduction in Climate Dynamics Michael Ghil Ecole Normale Supérieure, Paris, and University of California, Los Angeles
More informationG : Statistical Mechanics
G25.2651: Statistical Mechanics Notes for Lecture 15 Consider Hamilton s equations in the form I. CLASSICAL LINEAR RESPONSE THEORY q i = H p i ṗ i = H q i We noted early in the course that an ensemble
More information