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					Disentangling the Link Between Weather
              and Climate




             Ben Kirtman
      University of Miami-RSMAS
 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




                                                     Tropical Pacific
                                                     Rainfall (in box)


       Different SST    Different tropical atmospheric mean response
                         Different characteristics of atmos. noise
       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
Ensemble of N
AGCMs all
                  AGCM1               AGCM2                        AGCMN
receive same
OGCM-output
                                                       •••
SST each day    Sfc Fluxes1        Sfc Fluxes2                    Sfc FluxesN


                                                                                Average N
                                 average (1, …, N)                              members’ surface
                                                                                fluxes each day


                          Ensemble Mean Sfc Fluxes
                              OGCM receives ensemble average of
                                AGCM output fluxes each day


                                           SST

                                         OGCM



           Interactive Ensemble Approach
                       Interactive Ensemble
• Ensemble realizations of
  atmospheric component                                             M=1
  to isolate “climate signal”
                                                                          M=2
   Ensemble mean = Signal + 
• Ensemble mean surface fluxes                                             M=3
  coupled to ocean component
   – Ensemble average only applied
     at air-sea interface
   – Ocean “feels” an atmospheric
     state with reduced weather noise



                                                 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)
              Fixed
            1990 GHG




COLA CCSM-IE run       Full CCSM
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.

                            Variability Driven   Coupled Feedbacks?
                                by Noise            Ocean Noise?
 Ocean and Atmosphere Interactive
            Ensemble
                                           OGCMn Ensemble Member SST

 AGCM1                                     OGCM Ensemble Mean SST

●● ●                       Ensemble Mean
                               Fluxes
                                                       OGCM1
 AGCMN
                           Ensemble Mean
                                                     ●● ●
                               SST


                                                       OGCMM
         AGCMn Ensemble Member Flux

         AGCM Ensemble Mean Flux
 Impact of Ocean Internal Dynamics with Coupled Feedbacks


          SSTA Variability Due to Ocean Internal Dynamics




                                                            Enhanced
Reduced
          Climate Change Problem
                   Interactive Ensemble
Control 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?
 Conceptual Model

dTa                            Atmos → Ocean
      (To  Ta )  F
dt
dTo                                            <HF,(dSST)/dt>
      (Ta  To )  To       <HF,SST>
dt


                               Ocean → Atmos
dTa
      (To  Ta )
dt
dTo
      (Ta  To )  To  F
dt
Conceptual Model

                              Atmos → Ocean
 Atmosphere Forcing Ocean:
 • <HF(t), SST(t) > < 0
 • <HF(t), d(SST(t))/dt> <0                   <HF,(dSST)/dt>
                              <HF,SST>




                              Ocean → Atmos


Ocean Forcing Atmospere:
• <HF(t), SST(t) > > 0
• <HF(t), d(SST(t))/dt> > 0
                          <HF,SST>   Conceptual Model: Ocean →Atmos




                       <HF,dSST>


GSSTF2 Observational Estimates




  Area Averaged Fields Eastern
  Equatorial Pacific from GCMs


Prescribed SST is Reasonable
In Eastern Equatorial Pacific
                                       Conceptual Model: Atmos →Ocean
   <HF,dSST>



                         <HF,SST>


GSSTF2 Observational Estimates




Area Averaged Fields Central/Western
Equatorial Pacific



   CGCM Variability is too
   Strongly SST Forced
     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
Ensemble of N
AGCMs all
receive same
                  AGCM1               AGCM2                        AGCMN
OGCM-output
                                                       •••
SST each day    Sfc Fluxes1        Sfc Fluxes2                    Sfc FluxesN


                                                                                Average N
                                 average (1, …, N)                              members’ surface
                                                                                fluxes each day


                          Ensemble Mean Sfc Fluxes
                              OGCM receives ensemble average of
                                AGCM output fluxes each day


                                           SST

                                         OGCM



           Interactive Ensemble Approach
Ensemble of N
AGCMs all
receive same
                  AGCM1              AGCM2                         AGCMN
OGCM-output
                                                        •••
SST each day    Sfc Fluxes1       Sfc Fluxes2                     Sfc FluxesN


                                                                                Randomly select 1
                                   rand (1, …, N)                               member’s surface
                                                                                fluxes each day


                          Selected Member’s Sfc Fluxes
                                OGCM receives output of single,
                              randomly-selected AGCM each day


                                           SST

                                         OGCM



Random Interactive Ensemble Approach
                     Nino3.4 Power Spectra



           Moderate Stochastic                           Reduced Stochastic
           Atmospheric Forcing                           Atmospheric Forcing




  Period (months)                              Period (months)




            Increased Stochastic
            Atmospheric Forcing
                                   Increasing Stochastic Atmospheric
                                   Forcing Increase the ENSO Period




Period (months)
      Random IE                             Control
4                               4

3                               3


2                               2

1                               1
0                               0


-1                              -1

-2                              -2

-3                              -3

-4                              -4

     Nino34 Regression on Equatorial Pacific SSTA
    Random IE                             Control




Nino34 Regression on Equatorial Pacific Heat Content
                Contemporaneous Latent Heat Flux - SST Correlation

Observational                               Increased “Randomness”
Estimates                                   Coupled Model




Control
Coupled Model
                                            Random Interactive Ensemble:
                                            Increased the Whiteness of the
                                            Atmosphere 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:             Lower Resolution:
 IE     Control              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
                RMS(Obs)*1.4
                                                                 CFSIE
                                                                 RMSE


                                                        CFS
                                                        Spread
CFS
RMSE


                                                                 CFSIE
                                                                 Spread


  CFSIE - Reduce Noise Version (interactive ensemble) of CFS
                  Predictability Estimates
                                 Worst Case: Initial Condition
                                 Error (A+O) + Model Error
     Worst Case



                    Best Case


                                 Best Case: Initial Condition
                                 Error (A) + No Model Error



                                 Better Case: Initial Condition
Better Case                      Error (A) + Model Error


                     Best Case
 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 SST
  COLA
                                  COLA HF+CAM Winds




             Obs


CAM


                       COLA Winds+CAM HF
     ENSO Heat Content Anomalies


                                   OBS




              COLA                               CAM




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)

				
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posted:10/6/2011
language:English
pages:48