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Ben Kirtman
University of Miami-RSMAS
Disentangling the Link Between Weather and Climate
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)
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
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
SST Anomaly JFMA1998
SST Anomaly JFMA1989
Different SST Different tropical atmospheric mean responseDifferent characteristics of atmos. noise
Tropical Pacific Rainfall (in box)
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
• • •
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
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
Control Simulation: CCSM3.0 (T85, 1x1)300-year (Fixed 1990 Forcing)
Interactive Ensemble: CCSM3.0 (6,1,1,1)
Full CCSMCOLA CCSM-IE run
Fixed 1990 GHG
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.
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
Impact of Ocean Internal Dynamics with Coupled Feedbacks
EnhancedReduced
SSTA Variability Due to Ocean Internal Dynamics
Climate Change Problem
Control Ensemble
Interactive Ensemble
Interactive Ensemble
Climate of the 20th Century: Interactive - Control Ensemble
Global Mean Temperature Regression
Control Ensemble
Interactive Ensemble
Local Air-Sea Feedbacks: Point Correlation SST and Latent Heat Flux
“Best” Observational Estimate Coupled Model Simulation
Why Does ENSO Extend Too Far To The West?The Weather and Climate Link?
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>
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
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
Area Averaged Fields Central/WesternEquatorial Pacific
CGCM Variability is too Strongly SST Forced
<HF,dSST>
<HF,SST>
GSSTF2 Observational Estimates
Conceptual Model: Atmos →Ocean
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
• • •
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
• • •
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
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
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
Random IE Control
Nino34 Regression on Equatorial Pacific Heat Content
Contemporaneous Latent Heat Flux - SST Correlation
ObservationalEstimates
ControlCoupled Model
Increased “Randomness”Coupled Model
Random Interactive Ensemble:Increased the Whiteness of theAtmosphere forcing the Ocean
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)
Equatorial SSTA Standard Deviation
Low Resolution:IE Control
Lower Resolution:IE Control
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
CFSIE - Reduce Noise Version (interactive ensemble) of CFS
CFSIE - Reduce Noise Version (interactive ensemble) of CFS
RMS(Obs)*1.4CFSIERMSE
CFSSpread
CFSRMSE
CFSIESpread
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
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)
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
Time Mean Equatorial Pacific SSTCOLA
CAM
COLA Winds+CAM HF
COLA HF+CAM Winds
Obs
ENSO Heat Content Anomalies
OBS
CAMCOLA
COLA HF + CAM Winds COLA Winds + CAM HF
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)