Extending the Diagnosis of the Climate of the 20th

Century to Coupled GCMs

Edwin K. Schneider

George Mason University/COLA

Collaborators

Ben P. Kirtman

GMU/COLA

Zhaohua Wu

COLA

The Problem

• A primary area of C20C interest is predictability of low frequency climate variability (months).

• The primary tool of C20C is the state-of-the-art GCM

• The predictability of low frequency climate variability involves understanding the predictability of the coupled atmosphere/land/SST variability.

Current Approach

• Force AGCMs with observed 20th century boundary conditions (SST, sea ice, …), atmospheric composition (CO2, …).

• The forced responses of the atmosphere/land model to perfectly known boundary conditions are diagnosed by taking the ensemble mean.

• Compare the AGCM results to observed 20th century climate variability.

Result of This Approach

• The predictable “signal” to the specified boundary conditions plus an estimate of its predictability (signal/noise) given perfectly predicted boundary conditions.

Remaining Issues

• Predictability of the boundary conditions.

• Disturbing inconsistencies:– e.g. the sign of the surface heat flux of the

C20C ensemble over midlatitude oceans can be of opposite to the observed heat flux (Battisti).

– Apparently there are some important distinctions between the forced atmosphere and the mean atmosphere-ocean climate.

Alternative Approach I

• Could force OGCM with observed 20th century atmospheric boundary conditions.

• This will reveal the part of the SST variability forced by the atmosphere.

• If there is oceanic “noise” in the response, again we need ensembles of OGCM simulations.

• Results: determine what part of the SST variability is due to internal ocean variability (“noise”). The remaining variability must involve the atmosphere.

Alternative Approach II

• Diagnose 20th century variability using a coupled atmosphere/ocean GCM.

• But how? How can the atmospheric weather noise be controlled? How can a forcing be prescribed so that the coupled model could in principle reproduce observed 20th century climate variability event by event?

• The rest of the talk will describe a way to do this:– Turn the CGCM into the equivalent of an Intermediate

Coupled Model (ICM) and force with the 20th century noise.

Experimentation with a CGCM

• Change external forcing

• Change initial conditions

• No control over atmospheric “noise” (weather) due to chaotic nature of atmospheric dynamics

• No control over time evolution of SST or surface fluxes (part of solution)

Experiments with an Uncoupled GCM

• Relate models to observations of the evolution of the boundary conditions.– Forms the basis of model verification, predictability,

and dynamical understanding of the atmosphere and ocean (separately)

• AGCM– Specify time evolution of SST from observations

• OGCM– Specify time evolution of surface fluxes (wind stress,

heat flux, salinity flux)

Intermediate Coupled Models (ICM)

• Definition: Dynamical ocean model (e.g. OGCM) with atmospheric surface fluxes determined as a function of SST plus specified noise:

F=A(SST)+NWhere A is a statistical or empirical (physically based)

atmospheric model (e.g. Cane-Zebiak model)• Excellent for mechanistic experiments (e.g. role of

noise): Can specify noise externally as a function of time and space

• Provide the basis for our understanding of coupled atmosphere-ocean variability (ENSO, tropical Atlantic, midlatitude)

Limits of ICM

• Give ideas about possible mechanisms as a function of parameter choice– Small α implies ENSO forced by atmospheric noise– Large α implies ENSO is self sustaining with no

atmospheric noise forcing

• Assumptions concerning physical processes• Difficult to determine α except by tuning for best

match to observed variability (circular reasoning)

Model Comparison

• CGCM– Realistic

representation of dynamics and feedbacks

– Inflexible for diagnosis and understanding because noise is part of the solution

• ICM– Less realistic

representation of dynamics and feedbacks

– Highly flexible for diagnosis and understanding because noise can be specified

A New Class of Model

• A CGCM-class model has been designed by Ben Kirtman which has a realistic representation of dynamics, physics, and coupled feedbacks, but which can be used to ask the same mechanistic questions as the ICM

• “ICGCM” Intermediate CGCM or Interactive Ensemble “IE”

F=A(SST) + N where A is an AGCM-class model without noise

and noise N can be added externally

C20C Application for anICGCM

• What was the role of atmospheric noise in the observed decadal variability of SST 1950-present? – Was it entirely noise forced? – Was it due to some unstable coupled air-sea

mode? – What was the role of the different coupled

feedbacks?

• Each atmospheric model is forced by the

same SST and produces its own surface fluxes:

Fi=A(SST)+Wi(SST)

– Forced response A is the same for all i

– Weather noise Wi different for each model. Locally has properties of random noise Ni

• Ensemble mean flux F:

F=A(SST)+N

• As the number of atmospheres n becomes large, N0

• If variance of the weather noise is Vi=V for each AGCM, then the variance of the ensemble mean noise V is

VV/n

Basic Diagnostic Technique

1) Determine time and spatial evolution of the noise component of 20th century surface fluxes by subtracting the forced signal (C20C AGCM ensemble mean) from the estimated total surface fluxes (e.g. from reanalysis).

2) Force the ICgcM with the observed weather noise fluxes.

Theoretical Justification

• In the context of the Barsugli and Battisti 0D “null hypothesis” model, in which all SST variability is forced by weather noise and which includes coupled atmosphere ocean feedbacks, it can be proved that this approach will recover the “observed” SST variability.

• Proof available on request.

Preliminary Study

• Diagnosis of the mechanism for low frequency (>3 year period) North Atlantic SST variability in a CGCM

• Wu, Schneider, and Kirtman, 2004: Causes of Low Frequency North Atlantic SST Variability in a Coupled GCM. GRL (2004, in press); COLA Technical Report 160.

Models and Experiments

• COLA AGCM T42, 18 levels• GFDL MOM3 OGCM

– Standard “ARCs” physics– “Medium resolution” 1.5, better near equator, 25 levels– World ocean (non-polar) 74S - 65N– Climatological sea ice

• Anomaly coupled

• ICgcM: 6 copies of AGCM (initial conditions of each copy differ to produce uncorrelated weather noise)

• Century long simulations with CGCM and ICgcM

CGCM Simulation: Low Frequency (>3 Year) DJF SST Standard

Deviation

Observed Simulated

ICgcM Simulation

Quantitative Evaluation of Role of Noise Forcing

• Consider the ratio of SST varianceR=V(CGCM)/V(ICGCM)

• There are 6 members of the atmospheric model ensemble

• Therefore noise forcing of SST variability should be approximately 6x larger in CGCM than in ICGCM– In regions where SST variability is force by noise, R6– In regions where SST variability is due to coupled

dynamics, R1 (or so we initially thought)

Ratio of SST VarianceR=V(CGCM)/V(ICGCM)

• In regions where the CGCM has little low frequency SST variability, it is primarily forced by atmospheric noise.

• In regions where the low frequency SST variability is strong, it is not forced by atmospheric noise.– But we have not shown that the strong

variability is due to coupled (potentially predictable) ocean-atmosphere processes.

– To do this, we need to also eliminate ocean internal variability.

Internal Low Frequency Variability of the OGCM

• OGCM with climatological forcing (e.g. a “spin-up” simulation (order 40 years)

OGCM Internally Generated Low Frequency SST Variability

ICGCM Diagnosis

• Most of the low frequency SST variability in the ICGCM simulation (North Atlantic NDJFM) is caused by internal variability in the ocean (model).

• Atmospheric noise is of secondary importance in forcing the dominant pattern of variability.

• There is no evidence for unstable coupled feedbacks.• “The atmosphere leads the ocean” is not a good

diagnostic for distinguishing noise forced coupled variability.

• ICGCM needs modification to take ocean internally generated noise into account.

Implication for ICGCM

• Our ICGCM is incomplete. It does not filter out SST variability due to noise generated internally in the OGCM.

• Next generation ICGCM– Multiple copies of the ocean model– Atmosphere(s) see ensemble averaged SST– In development

Sfc. Fluxes1

AGCM1

Sfc. Fluxes2 Sfc. FluxesN

AGCM2 AGCMN

OGCM1 OGCM2 OGCMM

SST1 SST2 SSTM

Ensemble MeanSurface Fluxes

Ensemble MeanSST

Application of ICGCM to Diagnosis of Observed Low Frequency

Variability

• Force ICGCM with observed noise (atmospheric and oceanic)

• That part of the SST variability forced by the noise will be reproduced in detail

• That part of the SST variability due to unstable coupled processes will not be reproduced

• (We haven’t done this yet)

Determination of the Evolution of Atmospheric Noise

• The observed evolution of the climate system corresponds to a single realization of the atmospheric noise

• Surface fluxes can be decomposed as Total = Noise + Feedback from SST

• Feedback from SST can be determined from an ensemble of AGCM simulations forced by the observed evolution of SST (this is the standard C20C calculation)

• Total is estimated from observations/analysis/reanalysis• Noise can then be found as a residual

Noise = Total - Feedback from SST• Using the NCEP reanalysis, there is data to produce an

estimate of the time-dependent atmospheric noise 1950-present.

Example: Noise in a Single C20C Ensemble Member

• Single realization minus 10 member ensemble mean for COLA T63.

• No need to remove annual cycle.

• Surface heat flux.

• Note: not using observed fluxes.

Example of Atmospheric Noise

Determination of the Evolution of Oceanic Noise

• The observed evolution of the climate system corresponds to a single realization of the ocean noise

SST= SSTA + SSTO

• SSTA : forced by atmospheric fluxes• SSTO: internal ocean noise

• SST: total SST evolution is known• Find SSTA from an ensemble of uncoupled OGCM

simulations forced by observed atmospheric fluxes (never done?)

• Determine SSTO as a residual

Diagnostic Use of ICGCM

• Force ICGCM with observed noise: NO, NA.– If the coupled SST variability is completely noise

forced, then recover observed evolution of SST• This can be proved in the case of the Barsugli-Battisti zero-

dimensional coupled model

– If the simulated and observed SST evolutions are significantly different, then the residual is due to “unstable” coupled oscillations

• The result is model dependent• The observed data may not be good enough, so we will also

test this procedure in a perfect model framework

Evaluation of Coupled Feedbacks, Processes

• Can force ICGCM with NO and NA separately– Note that response to NO (internal ocean noise in

SST) will differ from NO because atmospheric feedback to NO will produce an SST response

• Can force with components of NA– Wind stress, heat flux, fresh water flux

• Can force regionally or temporally– NA in eastern tropical Pacific, evaluate response in

North Atlantic• Can examine dynamical processes

– Replace dynamical ocean with mixed layer ocean (globally or regionally)

Estimated Forced Atmospheric Response to

observed SST

Estimate Observed Noise

(Reanalysis – forced)

CONTROL: Interactive ensembleSST variability driven by the observed global noise

Local Noise ForcingSST variability driven by all the noise components in the North Atlantic

Effect of Noise SST variability driven by individual noise component in the North Atlantic only

Effect of Coupled FeedbackSST variability driven by noise but with prescribed annual cycle of a coupled flux component in the North Atlantic

Effect of Ocean DynamicsSST variability driven by heat flux noise only without oceanic dynamics (a mixed layer ocean in the North Atlantic)

DIAGNOSIS OF NOISE

Non-interactive Ensemble of 50

year AGCM simulations forced by

observed SST 1950-1999

CONTROL EXPERIMENT

Interactive Ensemble

Global Coupling

MECHANISM EXPERIMENTS

Interactive Ensemble in the North Atlantic only (50 year simulation)

Conclusions

• It is possible to combine GCM component models so that the resulting model has the diagnostic capabilities of an ICM, but with realistic coupled feedbacks

• In our coupled GCM, North Atlantic winter decadal SST variability is noise forced– Noise internal to the ocean is more important than

atmospheric weather noise

• This is a possible future avenue of exploration for diagnosis of 20th century climate variability which fits into the goals of the C20C project.