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Internal Atmospheric Dynamics and Climate Variability

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Title: Internal Atmospheric Dynamics and Climate Variability


1
Internal Atmospheric Dynamics and Climate
Variability
  • Ben Kirtman
  • George Mason University
  • Center for Ocean-Land-Atmosphere Studies

Acknowledgements R. Wu, S.-W. Yeh, D. Min, K.
Pegion and S. Kinter
2
  • How Does Internal Atmospheric Dynamics Impact
    Climate Variability?
  • One-Way Air-Sea Interactions (Atmospheric
    Stochastic Forcing or Noise)
  • Ocean as Thermodynamic Red Filter
  • Ocean-Dynamics Preferred Time scale
  • Two-Way Air-Sea Interactions
  • Coupled Feedbacks (stable) Noise
  • Coupled Feedbacks (stable) Noise Ocean
    Dynamics
  • Unstable Coupled Feedbacks

3
  • ENSO (Two-way Air-Sea Interactions)
  • Self Sustained (Unstable) or Damped ( Noise)
  • Damped Linear Dynamics Non-linearity ? Noise
  • Noise Amplitude Unrelated to Signal
  • Loss of Predictability due to Noise
  • Non-normality and Spatial Structure of Noise
  • Variations in Predictability Random
  • Unstable Non-linear Control of Growth
  • Loss of Predictability Chaos vs. Noise
  • Details of Noise Not Important
  • Signal and Noise Amplitude Dependence
  • Variations in Predictability Fundamental
  • Testing Generally Requires Ad-Hoc Assumptions
    About the Stochastic Forcing and Simplified
    Theoretically Motivated Models

Null-Hypothesis gt
4
Outline
  • Motivation and Definitions
  • Defining the Noise
  • The Interactive Ensemble Impact of Noise in a
    CGCM
  • Impact of the Noise
  • Global ENSO Teleconnections
  • Analysis of Variance (Damped vs. Unstable)
  • Ensemble Spread and Mean Predictability
    Implications
  • Low Frequency Variability in the Tropical Pacific
  • Stochastically Forced Variability
  • Decadal Variability of ENSO
  • Evidence for Important Non-linearities Properties

5
Defining the Noise Due to Internal Atmospheric
Dynamics
  • Atmospheric Ensemble Realizations

With Finite Ensembles Can Only Estimate Signal
and Noise
6
Using AGCM Ensembles to Isolated the Forced
Seasonal- to-Interannual Signal in Long Climate
Simulations
1 Ensemble Member
2 Ensemble Member
3 Ensemble Member
4,5,6 Ensemble Members
NCEP Analysis
7
Coupled GCM Ensemble Realizations
How to Separate Signal and Noise? Assumptions
about the Statistics of the Noise
72 Months
12 Months
8
Simple Coupled Model Perspective
? Atmos
Standard Coupled Model
? Ocean
? Atmos
Interactive Ensemble Coupled Model
? Ocean
9
Simple Coupled Model Perspective
? Atmos
Standard Coupled Model
? Ocean
Coupled Feedbacks
? Atmos
Interactive Ensemble Coupled Model
? Ocean
10
Simple Coupled Model Perspective
? Atmos
Standard Coupled Model
? Ocean
Lag-1 Auto-Correlation
? Atmos
Interactive Ensemble Coupled Model
? Ocean
11
Simple Coupled Model Perspective
? Atmos
Standard Coupled Model
? Ocean
Prescribed External Noise
? Atmos
Interactive Ensemble Coupled Model
? Ocean
12
CGCM Perspective
? Atmos
Standard Coupled Model
? Ocean
Noise is Internal to the System
? Atmos
Interactive Ensemble Coupled Model
? Ocean
13
Anomaly Coupled (i.e., correct climatology)
14
The tropical SST variability Standard Coupled
Interactive Ensemble Model
- The NINO3.4 (5?N-5?S,170?E-240?E) SST
variability
The Standard Coupled Model 200 yrs
The Interactive Ensemble Model 500 yrs
Self Sustained, but More Regular and More
Biennial
15
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16
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17
Stronger Tropical-Extratropical Telenconnections
Removing Too Much Noise?
Broader Meridional Scale
Reduced Heatflux Noise
18
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19
Analysis of Variance
Atmospheric Noise
Ignoring Lag-1 Auto-Correlation
Standard
Ocean Noise
Ocean Variance
Interactive
Ensemble Averaging
20
Large Ensemble Limit
M ? 8
If Oceanic Noise Comparable to Atmospheric Noise
Only Relatively Modest Reduction in Variance
Independent of Ensemble Size
21
Small Ocean Noise
If Small Ocean Noise and the Null Hypothesis is
Correct
22
Weak Coupling
Moderate Coupling
Variance Ratio
Strong Coupling
Ocean Noise/Atmos Noise
Null Hypthesis Ratio1/6
23
Including the Lag-1 Auto-Correlation
Weak Coupling
Moderate Coupling
Variance Ratio
Strong Coupling
1/6
Ocean Noise/Atmos Noise
24
What to Expect
  • If Ratio 1/M
  • Stochastically Forced System with Stable Coupled
    Feedbacks and Small Ocean Noise
  • If ½ lt Ratio lt 1
  • Relatively Large Internal Oceanic Noise?
  • Perhaps Unstable Coupled Feedbacks?
  • Simple Model Fails Important Non-Linearity?
  • If 1 lt Ratio
  • Unstable Coupled Feedbacks?
  • Simple Model Fails Important Non-Linearity?

25
s2 SSTA Standard Coupled
Variance Ratio Standard/Interactive
26
Are the Noise and Signal Related?
  • Stochastic Climate Model
  • Uncertainty and Signal are Independent
  • Uncertainty and Signal are Related
  • Non-Linearity?
  • Signal Measured by Ensemble Mean
  • Wind Stress in Nino4
  • Uncertainty Measured by Ensemble Spread
  • Wind Stress in Nino4

27
What to Expect
Interactive Ensemble Stochastic Coupled Model
28
Does the SST Depend on the Uncertainty in the
Atmosphere?
Unconditional Distribution Of Ocean Y
Distribution of Ensemble Spread X Of Atmosphere
Conditional Distribution Of Ocean Y
Does the Uncertainty in the Atmosphere Depend on
the SST?
Unconditional Distribution of Ensemble Spread X
Of Atmosphere
Unconditional Distribution of Ensemble Spread X
Of Atmosphere
Unconditional Distribution Of Ocean Y
29
What We Get From the Interactive Ensemble CGCM
30
Does the Uncertainty Depend on the Signal?
U-Stress (Nino4) Ensemble Spread (Distribution of
Uncertainty)
Conditional Distribution of Spread with an
Easterly Anomaly Small Spread
Unconditional U-Stress (Distribution of Signal)
Does the Uncertainty Depend on the Signal?
Conditional Distribution of Spread with an
Westerly Anomaly Large Spread
Gaussian
31
Does the Signal Depend on the Uncertainty?
U-Stress Ensemble Spread
SSTA Distribution Assuming Small Spread
(Uncertainty)
Unconditional SSTA
Does the Signal Depend on the Uncertainty?
U-Stress Ensemble Spread
SSTA Distribution Assuming Large Spread
(Uncertainty)
Unconditional SSTA
32
Composite 95-ile SST
Composite 5-ile SST
More Uncertainty in tx for Warm Events Compared
to Cold Events
Composite Zonal Wind Stress Spread
Based on 95-ile SST
Based on 5-ile SST
33
Predictability Implications
  • Cold Events More Predictable then Warm Events?

34
warm
Normal
cold
6 Months Lead
6 Months Lead
Reduced Noise
Standard Model
Nino3.4 ROC Curves
9 Months Lead
9 Months Lead
35
Testing The Null Hypothesis
  • Variance Ratios Regions of Tropical Indo-Pacific
    Variability that cannot be Explained by the Null
    Hypothesis (i.e. Ratio Larger than one)
  • Potential Importance of Ocean Noise
  • Uncertainty and Signal Strongly Related,
    Contradicting the Null Hypothesis
  • Cold Events more Predictable than Warm Events
  • Low Frequency Variations in ENSO Amplitude
    (Predictability)
  • Null Hypothesis Determined by Noise and
    Independent of Mean State Changes

36
Low Frequency Variability in the Tropical Pacific
  • Evidence for Stochastically Forced Modes
  • No Impact on ENSO
  • Evidence for Non-Linear Processes
  • ENSO Residual or Modulator?
  • Chicken and Egg Problem

37
Tropical decadal variability (10 year running
means) Standard Coupled Model
EOF1 46.4
EOF2 11.7
38
Time Series EOF1
10-Year Running Nino3 Variance
Time Series EOF2
10-Year Running Nino3 Variance
39
Warm Composite SSTA
Standard Coupled Model
COLD Composite SSTA
Difference in SSTA Variability
40
Interactive Ensemble Dominant Mode of Tropical
Pacific Variability
EOF1 41.6
EOF1 28.1
41
Interactive Ensemble Six Members
Time Series EOF1
10-year Running Nino3 Variance
Interactive Ensemble Twelve Members
Time Series EOF1
10-year Running Nino3 Variance
42
Warm Composite SSTA
Interactive Ensemble (6) Coupled Model
COLD Composite SSTA
Difference in SSTA Variability
43
Interactive Ensemble Six Members
10-year Running Nino3 Variance
Do the Changes in the Variance Relate to Any Mean
State Changes?
44
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45
High ENSO Variance SSTA Composite
Interactive Ensemble (6) Coupled Model
Low ENSO Variance SSTA Composite
Difference in SSTA Variability
46
Interactive Ensemble Six Members
EOF2 10.6
Interactive Ensemble Twelve Members
EOF2 22.8
47
Interactive Ensemble Six Members
EOF2 Time Series
10-year Running NINO3 Variance
Interactive Ensemble Twelve Members
EOF2 Time Series
10-year Running NINO3 Variance
48
EOF Amplitudes
EOF1
50
EOF1
40
Amplitude
EOF1
30
20
EOF2
10
Difficult to Detect
EOF2
IE-12
SC
IE-6
Decreasing Noise ?
? EOF1 Stochastically Forced Mode ? Independent
of ENSO ? EOF2 Non-Markovian ? Residual? or
Modulator?
49
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50
Markov Model Derived From Interactive Ensemble
(6) Output EOF1-EOF10
10-Year Running NINO3 Variance
Regression with SSTA
51
Concluding Remarks
  • Interactive Ensemble Mechanism for Controlling
    the Stochastic Forcing at the Air-Sea Interface
  • No Assumptions Regarding the Statistics of the
    Noise Required
  • Preserves the Internal Dynamics of the Component
    Models
  • SSTA Variance Ratios
  • Indo-Pacific Regions Where there Appears to be
    Unstable Coupled Interactions
  • Ensemble Mean and Ensemble Spread
  • More Wind Stress Uncertainty During Warm Events
  • Cold Events More Predictable
  • Non-Linear Processes
  • Low Frequency Variability in the Tropical Pacific
  • Dominant Mode of Variability Stochastically
    Forced
  • ENSO Variance Mode
  • Non-Linear Process
  • Residual? or Modulator?
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