Key Questions:

Decadal Climate Variability, Predictability, and Predictions - Focus on the Atlantic

Thomas L. DelworthGFDL/NOAA

CICS WorkshopJune 17, 2009

Key Questions:

1) What decadal predictability exists in the climate system, and what are the mechanisms responsible for that predictability?

2) Is the identified decadal predictability of societal relevance?

3) To what degree is the identified predictability (and associated climatic impacts) dependent on model formulation?

4) Are current and planned initialization and observing systems adequate for the job of initializing models for decadal prediction?

Outline

1. Components of decadal predictability

2. Climatic relevance of the Atlantic

3. Mechanisms of Atlantic variability

4. Underpinnings for decadal prediction

5. GFDL decadal predictability/prediction activities

Crucial points:

• Robust predictions will require sound theoretical understanding of decadal-scale climate processes and phenomena.

• Assessment of predictability and its climatic relevance may have significant model dependence, and thus may evolve over time (with implications for observing and initialization systems).

But … even if decadal fluctuations are not predictable, it is still important to understand them to better understand and interpret observed climate change.

Components of decadal variability and predictability

1. Forced climate change

• Predictability arising from estimates of future changes in radiative forcing agents, and the climate system response to those changes.

• “Committed warming” from past radiative forcing

2. Internal variability

• Decadal-scale fluctuations are an important part of climate variability

• Is there “meaningful” decadal-scale predictability in the climate system?

• Can we realize that predictability?

• Is the “natural” variability altered by radiative forcing?

More intense hurricanes

Drought

More rain over Saheland western India

Warm North Atlantic linked to …

Two important aspects:a. Decadal-multidecadal fluctuationsb. Long-term trend

North Atlantic Temperature

What will the next decade or two bring?

Why look to the Atlantic for decadal predictability?

Impacts of Atlantic Multidecadal Variability

Simulated atmospheric response to imposed SST anomalies in the Atlanticusing the Hadley Centre atmospheric model (HADAM3). Aug-Oct.

Sutton and Hodson, 2005.

SLP Rainfall Air Temperature

JJA Precipitation Anomalies Associated with Maximum MOC

Units: cm/day

Paleo and modern records show global scale impact of Atlantic SST anomalies on rainfall, SST, and circulation.

Projected Atlantic SST Change (relative to 1991-2004 mean)

Results from GFDL CM2.1 Global Climate Model

Can we predict which trajectory the real climate system will follow?

What is the mechanism behind Atlantic multidecadal variability?

Two different, but not mutually exclusive, ideas:

1. Atlantic Multidecadal Variability is a product of internal variability of the climate system through multidecadal scale strengthening and weakening of the Atlantic Meridional Overturning Circulation (AMOC).

2. Atlantic Multidecadal Variability is a product of changing radiative forcing (greenhouse gases and aerosols) in the 20th century.

“Comparing the results to observations, it is argued that the long-term, observed, North Atlantic basin-averaged SSTs combine a forced global warming trend with a distinct, local multi-decadal “oscillation” that is outside of the range of the model-simulated, forced component and most likely arose from internal variability.”Ting et al, in press, Journal of Climate, 2008

Kravtsov (2007) came to similar conclusion

Pattern of variability after removal of forced signal.

SST anomalies associated with interdecadal MOC fluctuations

EOF 1 HADISSTOBSERVED SST

MODEL

ModestTropicalAmplitude

Simulated North Atlantic AMOC Index

Aerosol only forcingAll forcings

Greenhouse gas only forcing

106 m

3 s

-1 (S

verd

rup

s)

Delworth and Dixon, 2006

Decadal Predictability and Prediction Efforts

1. Characterizing and understanding decadal variability, climatic impacts, and interactions with radiative forcing

2. Idealized predictability experiments- inherent predictability of AMOC and other phenomena- climatic relevance

3. New coupled assimilation methods for reanalysis and initialization of predictions

4. Improved models- higher resolution

- new physics/numerics- reduced bias

5. Prototype decadal predictions and attribution

Focus on Atlantic, but methodology is general.

Atlantic Meridional Overturning Circulation (AMOC) in GFDL CM2.1 Model

Spectrum of AMOC

106

m3

s-1 (

Sve

rdru

ps)

Predictability of Atlantic MeridionalOverturning Circulation(AMOC) in GFDL CM2.1 Climate Model

AM

OC

Ind

ex

AM

OC

Ind

ex

Surface Air Temperature

Rainfall(cm/day)

June-July-August

Years 3-10 after commonocean initialization

Wet central US

Dry Sahel

Histogram of central US temperature

ControlJJA

Fre

quen

cy

of

occ

urre

nce

Histogram of NH Extratropical Mean Surface Air Temperature

Two ensembles of projections for 2001-2010 using same forcing.

They differ due to predictable decadal variability, primarily associated with Atlantic Meridional Overturning Circulation.

Fre

quen

cy o

f oc

curr

ence

Temperature

Atmosphere modelu, v, t, q, ps

Ocean modelT,S,U,V

Sea-Ice model

Land model

τx,τy (Qt,Qq)

Tobs,Sobs

GHGNA forcings

a)

Prior PDF

Analysis PDF

DataAssim(Filtering)

obs PDF

y obx

axb)

uo, vo, to

Coupled Ensemble Data Assimilation estimates the temporally-evolving probability distribution of climate states under observational data constraint:

Multi-variate analysis maintaining physical balances between state variables such as T-S relationship - geostrophic balance mostly

Ensemble filter maintaining the nonlinearity of climate evolution mostly

All coupled components adjusted by observed data through instantaneously-exchanged fluxes

Optimal ensemble initialization of coupled model with minimum initial shocks

Pioneering development of coupled data assimilation system

OAR 2008 Outstanding Paper Award: S. Zhang, M. J. Harrison, A. Rosati, and A. Wittenberg

New coupled assimilation system

NINO3 Anomaly Correlation Coefficient

0.6

New coupled assimilation system dramaticallyimproves ENSO prediction skill

GFDL participating in development of CTB/NCEP/National multi-model forecast system

Fore

cast

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(mon

ths)

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3

5

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11Fo

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ead

Tim

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J F M A M J J A S O N DForecast Start Month

Traditional assimilation system

J F M A M J J A S O N DForecast Start Month

Model development

• Simulated variability and predictability is likely a function of the model

• Developing improved models (higher resolution, improved physics) is crucial for studies of variability and predictability

• New model: CM2.4

– Ocean: • Resolution: 25Km in tropics to 10 Km high latitudes• Very energetic, low viscosity, higher order advection

– Atmosphere: Global, 1 degree• Plans to explore coupling 50 Km atmosphere model

(CM2.5)

Key issue: How sensitive is simulated decadal variability and predictability to model resolution and physics?

GFDL CM2.4 Global Coupled ModelSST, surface currents

GFDL CM2.1 Global Coupled ModelSST, surface currents

Ocean resolution as fine as 10Km in high latitudes

GFDL CM2.1 model was one of the best in the world for Atlantic simulations in AR4. Even so, important processes are not well resolved.

Sfc currents and SST

Observational estimate (satellite)

GFDL CM2.4 Model

Eddy Kinetic Energy [Log scale, cm2 s-2]

High resolution coupled model shows realistic simulation of eddy kinetic energy

Courtesy Riccardo Farnetti

Discussion

• Decadal prediction/projection is a mixture of boundary forced and initial value problem

• Changing radiative forcing (esp. aerosols) will be a key ingredient

• Some basis for decadal predictability of internal variability, probably originating in ocean

• Some of predictability will arise from unrealized climate change already in the system

• Substantial challenge for models, observations, assimilation systems, and theoretical understanding

Current/planned activities at GFDL

• Ongoing studies with CM2.1 model to develop improved understanding of

a) mechanisms of simulated decadal variability

b) decadal scale predictability arising from internal variability

• Development and use of higher resolution coupled models. Want this to be focus of variability, predictability and prediction efforts

• Development of new coupled assimilation system

• Assessment of observation systems for decadal predictability

• Prototype decadal predictions planned for Spring, 2009

• Assess predictability and predictions in collaboration with efforts at NCAR and MIT

GFDL Decadal Prediction Research in support of IPCC AR5

Key goal: assess whether climate projections for the next several decades can

be enhanced when the models are started from observed state of the climate

system. Is there a predictable component of decadal variability that could be

realized?

(a) Use “workhorse” CM2.1 model from IPCC AR4 [2009]

Decadal hindcasts from 1980 onwards (10 member ensembles)

Decadal predictions starting from 2001 onwards (10 member ensembles)

(b) Use experimental high resolution model [2010]

Long control simulation and examination of predictability

Decadal predictions starting from 2001 onwards (10 member ensembles)

(c) Use CM3 [2010, tentative; if scientifically warranted and resources permit]

Decadal predictions starting from 2001 onwards (10 member ensemble)

Cautionary Notes• This field is in its infancy – many fundamental challenges

remain

• Results from CMIP5 decadal predictions should be viewed with caution in light of:

• Model bias and drift and their impacts on prediction• Varying initialization strategies• Unknown level of true predictability of the system

• It is possible that initial decadal prediction attempts will show little or no “meaningful” predictability (from internal variability). That would lead to at least two possibilities:

1. The system is not predictable on decadal time scales2. We are not yet able to realize that predictability

Will we be able to distinguish between these two possibilities?

Final points …

• Decadal prediction is a major scientific challenge.

• If scientific progress warrants, decadal prediction needs to eventually involve complete Earth System Models including biogeochemical processes.

• An equally large challenge is evaluating and understanding the possible utility of decadal predictions.

– What can we say about evolution of Atlantic SST?

– What can we say about the likelihood of North American drought over the next 1-10 years?

– “Early Warning System” for possibly abrupt climate change

Predictability of Atlantic MeridionalOverturning Circulation(AMOC) in GFDL CM2.1 Climate Model

30

Observational AMOC Analyses

Model-based relationships between subpolar gyre and AMOC are used to assess observed changes and create statistical prediction model.

Zhang, 2008

D. Model Development

a. CMDT (CM3,CM2M,CM2G)

b. High resolution coupled models (CM2.4,CM2.5)

Simulated variability and predictability is likely a function of the model resolution and physics

Developing improved models (higher resolution, improved physics, reduced bias) is crucial for studies of variability and predictability

Ocean Atmos Computer Status

CM2.4 10-25 Km 100 Km GFDL Running

CM2.5 10-25 Km 50 Km DOE In development

CM2.6 4-10 Km 50 Km DOE In development

GFDL Argo DB [monthly update]

Step 1: Data Mirroring System( Identified Argo + GTSPP )

Step 2: Quality Control System( Real Time + Delayed Mode )

Step 3: Coupled DataAssimilation System

[QC Process]

[DMQC result]

[GFDL Argo DB]

Courtesy of You-Soon Chang

• GFDL R15, R30 40-80 years (Delworth et al., 1993, 1997)• GFDL CM2.1 20 years

• HADCM3 25 years (Dong and Sutton, 2005)• HADCM3 centennial (Vellinga and Wu, 2004; Knight et al., 2005)

• NCAR CCSM3 20 years (Danabasoglu, 2008)

• ECHAM3 35 years (Timmermann et al., 1998)• ECHAM5 70-80 years (Jungclaus et al., 2005)

• Theoretical work in hierarchy of models: te Raa et al. (2004)

Multiple physical processes influencing the Atlantic MOC may contribute to the variety of timescales found.

What do coupled models tell us about internal variability in the Atlantic?

Many models simulate enhanced multidecadal variability involving Atlantic MOC* Similar spatial structure as observations* Differing timescales in the multidecadal range, differing mechanisms* Large-scale atmospheric impact

Forecast for NINO 3.4 SST, Initial Conditions from 1 March, 2009.

GFDL CM2.1 Model.