Disentangling the Link Between Weather and Climate

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



Climate of the 20th Century: Interactive - Control Ensemble

Global Mean Temperature Regression

Control 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


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


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



• How to Examine Noise within Context of a Coupled GCM?– Typical Climate Resolution (T85, 1x1)– Atmospheric Noise, Oceanic Noise, Climate Change

Problem



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



• How to Examine Noise within Context of a Coupled GCM?– Typical Climate Resolution (T85, 1x1)– Atmospheric Noise, Oceanic Noise, Climate Change

Problem



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



• 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



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Disentangling the Link Between Weather and Climate