Page 1 Hadley Centre © Crown copyright 2004 Evidence for the Atlantic Multidecadal Oscillation as an internal climate mode from coupled GCM simulations

© Crown copyright 2004 Page 1

Hadley Centre

Evidence for the Atlantic Multidecadal Oscillation as an internal climate mode from

coupled GCM simulationsJeff Knight

Hadley Centre, Exeter, UK

4th International CLIVAR Climate of the 20th Century Workshop,

Hadley Centre, Exeter, UK

Wednesday, 14th March 2007


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AMO in observations


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AMO in observations

Mean North Atlantic SST ‘AMO index’

Low-pass (> 13.3y) filtered detrended HadISST

Larger than trend or interannual

Surface temperature anomaly Regression 1870-1999

HadCRUTv blended SST/air temp 90% confidence interval accounting for

autocorrelation

Palaeoclimate – AMO back to C16-17th? Tree rings (Gray et al., 2004) Multiproxy (Delworth and Mann, 2000)

Models show some THC-SST links e.g. Delworth and Mann, 2000

Is the AMO long-lived/periodic?Forced or internal?


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AR4 20th Century Forcings Coupled Ensembles


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AR4 Ensembles

Single model North Atlantic mean SST

Grey = annual means for 3 ensemble membersRed = ensemble meanBlack = 90% limits of estimated ens meanBlue = Observed SST from HadSST2All data relative to 1900-99 average

• Example of an AR4 20c3m ensemble with few members

• High inter-member variability leads to very broad uncertainty in the ensemble mean

• Not easy to distinguish the observations from the possible model estimates of the forced response


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AR4 Ensembles

Multi-model ensemble

Red = super-ensemble mean (34 members)Black = 90% limits of estimated ens meanBlue = Observed SST from HadSST2All data relative to 1900-99 average

• Super-ensemble (34) using data from 11 models with natural + anthro forcings and available SST

• Narrower uncertainty on ensemble mean

• Range is now a function of both internal climate variability and model and forcing differences

• Represents a ‘best estimate’ of the forced response

• Atlantic SST is inconsistent with the forced response for much of the last 150 years


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AR4 Ensembles

North Atlantic SST trends

• Obs show clear multidecadal trend oscillations

• Model trends are present but relatively weak

• Difference therefore resembles obs

• Obs trends almost always significantly different from the forced signal

OBS

AR4AVG

OBS minusAR4

Linear change = Trend (K/year) x Period (year)


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AR4 Ensembles

A better AMO index?

Black = 90% limits of estimated ens meanBlue = Observed SST from HadSST2All data relative to 1900-99 average

Removing a model-based estimate of the historical forced response as an improvement on

• linear detrending

• subtracting ‘background’ estimates based on global mean temperature


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AR4 Ensembles

Implications

The inconsistency between observed North Atlantic SST and the ensemble estimate of the forced response suggests several possibilities:

*In the latter 2 cases, the errors would have to be specific to the Atlantic as the models perform well for the global mean

• The AMO is an internal mode

• Models are inadequate to represent the effects of known forcings on climate*

• The forcings used are incorrect or incomplete*


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HadCM3 Control Simulation


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Control Simulation

1400 Year Coupled Model Representation of the AMO70-180 Year band Observed AMO Pattern

0°

60°

120°

180°

Similar pattern and time scale to observed AMO fluctuations.

Similar magnitude – North Atlantic low frequency (>45 year) standard deviation is 0.10K, 0.14K in observations.

Observed AMO likely to be long-lived climate mode.


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Control Simulation

1400 Year HadCM3 control simulation

Maximum overturning streamfunction at 30°N Persistent band of variability between 70-120 years Compares with observed period of ~65 years (instrumental) and 40-130 years (palaeo – Gray et al. 2004).


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Large-scale temperatures


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Control Simulation

THC-Mean temperature cross-correlations

NorthernHemisphere

SouthernHemisphere

Global

0.09°C Sv-1 (0.55) 0.01°C Sv-1 (0.13) 0.05°C Sv-1 (0.59)

Suggests potential predictability of climate for several decades into the future


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Mechanism


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Mechanism

Density anomalies related to the THC

Regress 0-800m averaged density onto THC

At THC peak, high densities in the mid-latitude and sub-polar ocean

Low densities in sub-tropical ocean

Density anomalies at 60°N mostly result from the contribution of salinity anomalies, rather than thermal anomalies.

From Vellinga and Wu (2004)


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Mechanism

Coupled ocean-atmosphere interactions

Precipitation change associated with an ITCZ shift caused by SST anomalies supplies the tropical fresh water flux forcing

Coupled mechanism involving a delayed oceanic salinity feedback.

From Vellinga and Wu (2004)


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Climate Impacts


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Climate Impacts

North East Brazil Rainfall

NE Brazil has large multidecadal wet season (MAM) rainfall variability

Simulated ITCZ shifts north and away when N Atlantic warm (AMO+) drier NE Brazil

Simulated rainfall changes similar in size to observations


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Climate Impacts

Sahel Rainfall

African Sahel has large multidecadal rainfall variability

JJA simulated ITCZ shifts north when N Atlantic warm (AMO+) wetter Sahel

Simulated changes about one-third of those observed.

Compare ITCZ shifts with Caribbean palaeo salinity variations (Schmidt et al., 2004).


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Climate Impacts

North Atlantic-European circulation response to the AMO

Simulated MSLP

regression with AMO index

DJF

MAM

JJA

SON

Simulated precipitation

regression with AMO index

• No winter NAO signal at any lead/lag

• Anomalies typically smaller than observed

multidecadal NAO change

Broadest signal in summer and autumn

• Summer/Autumn signal in Europe

• Little sign of US summer signal (Sutton & Hodson,2005)


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Climate Impacts

Atlantic Hurricanes – the observed relationship

Goldenberg et al. (2001) claim a link between the frequency of major Atlantic hurricane formation and AMO variations in North Atlantic SST.

Suggest AMO affects vertical shear in the hurricane formation region via circulation changes

1944 1998

Ma

jor

Hu

rric

an

es

Emanuel (2005) suggests a more direct link between SST and the integrated intensity of storms.


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Climate Impacts

Atlantic Hurricanes – obs model comparisons

Model supports an AMO relationship with hurricane development shear, but also shows an IPO relationship. AMO and IPO are uncorrelated (0.06).

NCEP/NCAR reanalysis 200-850

hPa shear

August-October (ASO) (1951-60)-(1971-80)

HadCM3 decadal AMO-shear correlation

Goldenberg main development area

highlighted

HadCM3 AMO index (red),

versus mean Goldenberg area

shear (black)

Correlation of simulated main

development area shear with SST


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Conclusions


Hadley Centre

Conclusions

The AMO is inconsistent with an estimate of the response of Atlantic SST to natural and anthropogenic forcings from the AR4 models

Either the AMO is internal or the models or their forcings are wrong

This analysis shows an increasing AMO in recent decades

A 1400 year HadCM3 control simulation suggests the AMO is a long-lived coupled mode of climate variability associated with modern-day variations in the strength of the THC

Diagnosis of the simulated mechanism reveals a delayed salinity feedback via displacements of ITCZ rainfall caused by THC-related temperature anomalies

The simulation confirms AMO links with a range of important regional climate phenomena such as NE Brazil and Sahel rainfall, Atlantic Hurricane formation and European circulation.


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Questions & Answers


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Climate Impacts


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Reconstruction and Forecast of the THC


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Reconstruction and forecast of the THC

Use HadCM3 simulation to make a statistical model

between SST-THC

Use SST from HadISST dataset to reconstruct running decadal THC

1870-2002

Decadal Northern North Atlantic SST as a

statistical predictor


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Reconstruction and forecast of the THC

Look for points in the control simulation where the THC index rises through present day (decade 1997-2003) reconstructed value (0.63 Sv)

Track the subsequent THC evolution for each of these ‘analogues’ for 6 decades.

Use these to represent the next ~35 years (observed period shorter than in model).

Natural downturn in THC in next decade, to levels of 1960s before 2030 (on average -0.70 Sv)

THC Predictability


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Motivation

Large scale SST patterns (after Folland et al., 1999)

HadISST Low-pass (> 13.3y) EOFs

1911-2002

40ºS - 70ºN

Projections 1870-2002


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Control Simulation

Coupled Model Representation of the AMO70-180 Year band 25-125 Year band

0°

60°

120°

180°


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AR4 Ensembles

North Atlantic mean temperature

North Atlantic (0°-80°W, 10°-70°N) Annual mean SST 4 member ensemble with HadCM3 (black) Solar+Volc+Anthro. Stott et al. (2000) Observed SST data from HadISST (blue)

Centre year of 30-year trend

Year

Te

mp

era

ture

(°C

)T

ren

d (

°C d

ec

ad

e-1)

Anomalies difficult without bias 30 year trends Uncertainty in ensemble mean trend 90% limits (shaded) Inconsistent (1900-1930) to (1925-1955) Also (1945-1975) to (1965-1995) Uncertainty still large with 4 members


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Mechanism

Salinity leading density anomalies

0-800m salinity contribution to density regressed onto zonal mean density at 60°N

First signs of positive salinity anomalies in subtropics 6 decades (half a period) before a THC peak


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Mechanism

Salinity budget analyses

0-800m mean salinity driven density tendencies regressed onto the THC

In tropics (0-35°N) density increases ~ 6 decades before the peak THC, induced by surface flux forcing and removed by transport

In mid-latitudes (35-48°N) density increases ~ 4 decades before, caused by transport and removed by surface flux forcing

Sub-polar (48-65°N) density increases ~ 2 decades before by transport


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Mechanism

Transport time scale

100 year run with a unit tracer at the surface between 0-15°N

Follow tracer concentrations averaged over 0-800m

Slow buildup in sub-polar ocean

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Page 1 Hadley Centre © Crown copyright 2004 Evidence for the Atlantic Multidecadal Oscillation as an internal climate mode from coupled GCM simulations