ben kirtman university of miami-rsmas disentangling the link between weather and climate
TRANSCRIPT
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Ben Kirtman
University of Miami-RSMAS
Disentangling the Link Between Weather and Climate
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Noise and Climate Variability• What Do We Mean By “Noise” and Why Should
We Care?– Multi-Scale Issue
• How to Examine Noise within Context of a Coupled GCM- Interactive Ensemble– Typical Climate Resolution (T85, 1x1)– Ex: Atmospheric Noise, Oceanic Noise, ENSO
Prediction, Climate Change
• Resolution Matters– Noise Aliasing
• Quantifying Model Uncertainty (Noise)
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Why is Noise an Interesting Question?
• Large Scale Climate Provides Environment for Micro- and Macro-Scale Processes– Local Weather and Climate: Impacts, Decision
Support
• Micro- and Macro-Scale Processes Impact the Large-Scale Climate System– Interactions Among Climate System Components– Justification for High Resolution Climate Modeling
• But, this is NOT the Definition of Noise– Noise Occurs on all Space and Time Scales
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How Should Noise be Defined?
• Use ensemble realizations
– Ensemble mean defines “climate signal”
– Deviation about ensemble mean defines Noise
– Climate signal and noise are not Independent
– Examples: • Atmospheric model simulations with prescribed SST• Climate change simulations
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SST Anomaly JFMA1998
SST Anomaly JFMA1989
Different SST Different tropical atmospheric mean responseDifferent characteristics of atmos. noise
Tropical Pacific Rainfall (in box)
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Modeling Weather & Climate Interactions
• Previously, this required ad-hoc assumptions about the weather noise and simplified theoretically motivated models
• We adopt a coupled GCM approach– Weather is internally generated
• Signal-noise dependence
– State-of-the-art physical and dynamical processes
Interactive Ensemble
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• • •
SST
OGCM
average (1, …, N)
Sfc Fluxes1
AGCM1
Sfc Fluxes2
AGCM2
Sfc FluxesN
AGCMN
Ensemble Mean Sfc Fluxes
Interactive Ensemble Approach
Ensemble of N AGCMs all receive same OGCM-output SST each day
OGCM receives ensemble average of AGCM output fluxes each day
Average N members’ surface fluxes each day
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Interactive Ensemble• Ensemble realizations of
atmospheric component
to isolate “climate signal”
Ensemble mean = Signal + • Ensemble mean surface fluxes
coupled to ocean component– Ensemble average only applied
at air-sea interface
– Ocean “feels” an atmospheric
state with reduced weather noise
M=2
M=1
M=3
M=4, 5, 6
M = number of atmospheric ensemble members
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Control Simulation: CCSM3.0 (T85, 1x1)300-year (Fixed 1990 Forcing)
Interactive Ensemble: CCSM3.0 (6,1,1,1)
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Full CCSMCOLA CCSM-IE run
Fixed 1990 GHG
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Variability Drivenby Noise
Coupled Feedbacks?Ocean Noise?
If all SST variability is forced by weather noise, the ratio of SST variance (IE CGCM)/(Standard CGCM) is expected to be 1/6 and the ratio of standard deviations to be 0.41.
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Ocean and Atmosphere Interactive Ensemble
AGCM1
AGCMN
●●● Ensemble MeanFluxes
OGCM1
OGCMM
●●●Ensemble Mean
SST
AGCMn Ensemble Member Flux
AGCM Ensemble Mean Flux
OGCMn Ensemble Member SST
OGCM Ensemble Mean SST
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Impact of Ocean Internal Dynamics with Coupled Feedbacks
EnhancedReduced
SSTA Variability Due to Ocean Internal Dynamics
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Climate Change Problem
Control Ensemble
Interactive Ensemble
Interactive Ensemble
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Climate of the 20th Century: Interactive - Control Ensemble
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Global Mean Temperature Regression
Control Ensemble
Interactive Ensemble
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Local Air-Sea Feedbacks: Point Correlation SST and Latent Heat Flux
“Best” Observational Estimate Coupled Model Simulation
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Why Does ENSO Extend Too Far To The West?The Weather and Climate Link?
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dT
dtT T
dT
dtT T T F
ao a
oa o o
= −
= − − +
α
β γ
( )
( )
dT
dtT T F
dT
dtT T T
ao a
oa o o
= − +
= − −
α
β γ
( )
( )
Conceptual Model
<HF,(dSST)/dt>
Atmos → Ocean
Ocean → Atmos
<HF,SST>
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Conceptual Model
<HF,(dSST)/dt>
Atmos → Ocean
Ocean → Atmos
<HF,SST>
Atmosphere Forcing Ocean:• <HF(t), SST(t) > < 0• <HF(t), d(SST(t))/dt> <0
Ocean Forcing Atmospere:• <HF(t), SST(t) > > 0• <HF(t), d(SST(t))/dt> > 0
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Area Averaged Fields EasternEquatorial Pacific from GCMs
GSSTF2 Observational Estimates
Prescribed SST is ReasonableIn Eastern Equatorial Pacific
<HF,SST>
<HF,dSST>
Conceptual Model: Ocean →Atmos
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Area Averaged Fields Central/WesternEquatorial Pacific
CGCM Variability is too Strongly SST Forced
<HF,dSST>
<HF,SST>
GSSTF2 Observational Estimates
Conceptual Model: Atmos →Ocean
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Western Pacific Problem
• Hypothesis: Atmospheric Internal Dynamics (Stochastic Forcing) is Occurring on Space and Time Scales that are Too Coherent
Too Coherent Oceanic Response
Excessive Ocean Forcing Atmosphere
Test: Random Interactive Ensemble
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• • •
SST
OGCM
average (1, …, N)
Sfc Fluxes1
AGCM1
Sfc Fluxes2
AGCM2
Sfc FluxesN
AGCMN
Ensemble Mean Sfc Fluxes
Interactive Ensemble Approach
Ensemble of N AGCMs all receive same OGCM-output SST each day
OGCM receives ensemble average of AGCM output fluxes each day
Average N members’ surface fluxes each day
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• • •
SST
OGCM
rand (1, …, N)
Sfc Fluxes1
AGCM1
Sfc Fluxes2
AGCM2
Sfc FluxesN
AGCMN
Selected Member’s Sfc Fluxes
Random Interactive Ensemble Approach
Ensemble of N AGCMs all receive same OGCM-output SST each day
OGCM receives output of single, randomly-selected AGCM each day
Randomly select 1 member’s surface fluxes each day
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Nino3.4 Power Spectra
Period (months) Period (months)
Period (months)
Increasing Stochastic AtmosphericForcing Increase the ENSO Period
Reduced Stochastic Atmospheric Forcing
Moderate Stochastic Atmospheric Forcing
Increased Stochastic Atmospheric Forcing
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ControlRandom IE
Nino34 Regression on Equatorial Pacific SSTA
0 0
1
2
-1
-2
-3
-4 -4
-3
-2
-1
1
2
3
4
3
4
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Random IE Control
Nino34 Regression on Equatorial Pacific Heat Content
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Contemporaneous Latent Heat Flux - SST Correlation
ObservationalEstimates
ControlCoupled Model
Increased “Randomness”Coupled Model
Random Interactive Ensemble:Increased the Whiteness of theAtmosphere forcing the Ocean
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Noise and Climate Variability• What Do We Mean By “Noise” and Why Should
We Care?– Multi-Scale Issue
• How to Examine Noise within Context of a Coupled GCM?– Typical Climate Resolution (T85, 1x1)– Atmospheric Noise, Oceanic Noise, Climate Change
Problem
• Resolution Matters– Noise Aliasing
• Quantifying Model Uncertainty (Noise)
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Equatorial SSTA Standard Deviation
Low Resolution:IE Control
Lower Resolution:IE Control
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Understanding Loss of Forecast Skill
• What is the Overall Limit of Predictability?• What Limits Predictability?
– Uncertainty in Initial Conditions: Chaos within Non-Linear Dynamics of the Coupled System
– Uncertainty as the System Evolves: External Stochastic Effects
• Model Dependence?– Model Error
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CFSIE - Reduce Noise Version (interactive ensemble) of CFS
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CFSIE - Reduce Noise Version (interactive ensemble) of CFS
RMS(Obs)*1.4CFSIERMSE
CFSSpread
CFSRMSE
CFSIESpread
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Worst Case: Initial ConditionError (A+O) + Model Error
Worst Case
Better Case: Initial ConditionError (A) + Model Error Better Case
Best Case: Initial ConditionError (A) + No Model Error
Best Case
Best Case
Predictability Estimates
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Noise and Climate Variability• What Do We Mean By “Noise” and Why Should
We Care?– Multi-Scale Issue
• How to Examine Noise within Context of a Coupled GCM?– Typical Climate Resolution (T85, 1x1)– Atmospheric Noise, Oceanic Noise, Climate Change
Problem
• Resolution Matters– Noise Aliasing
• Quantifying Model Uncertainty (Noise)
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Multi-Model Approach to Quantifying Uncertainty
• Multi-Model Methodologies Are a Practical Approach to Quantifying Forecast Uncertainty Due to Uncertainty in Model Formulation
• No Determination of Which Model is Better - Depends on Metric
• Taking Advantage of Complementary or Orthogonal “Skill”
• Taking Advantage of Orthogonal Systematic Error
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Time Mean Equatorial Pacific SSTCOLA
CAM
COLA Winds+CAM HF
COLA HF+CAM Winds
Obs
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ENSO Heat Content Anomalies
OBS
CAMCOLA
COLA HF + CAM Winds COLA Winds + CAM HF
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Noise and Climate Variability• What Do We Mean By “Noise” and Why Should
We Care?– Multi-Scale Issue
• How to Examine Noise within Context of a Coupled GCM- Interactive Ensemble– Typical Climate Resolution (T85, 1x1)– Ex: Atmospheric Noise, Oceanic Noise, ENSO
Prediction, Climate Change
• Resolution Matters– Noise Aliasing
• Quantifying Model Uncertainty (Noise)
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