An Assessment of Multidecadal North Atlantic Sea Surface Temperature Variability
in Global Climate Models
N.G. Heavens1,*, K.F. Li1, M.-C. Liang2, L.-C. Lin2, K.-K. Tung3, Y.L. Yung1
1
Division of Geological and Planetary Sciences, California Institute of Technology, MC 150-21,
Pasadena, CA, 91125
2
Research Center for Environmental Changes, Academia Sinica, 128 Sec. 2, Academia Rd.,
Nankang, Taipei 115, Taiwan
3
Department of Applied Mathematics, University of Washington, Box 352420, Seattle, WA,
98195
*Corresponding author: heavens@gps.caltech.edu
To be submitted to Geophys. Res. Lett.
Abstract: The Atlantic Multidecadal Oscillation (AMO) is the leading mode of non-ENSO
variability in sea surface temperatures. It is difficult to create and validate a dynamical theory of
the AMO due to the sparseness and brevity of the present day instrumental record; an alternate
approach has been to investigate model simulations that spontaneously contain multi-decadal
Atlantic Ocean variability for insight into the dynamics of the observed AMO. Here, we analyze
the pre-industrial control runs made for the Intergovernmental Panel on Climate Change’s
(IPCC) Fourth Assessment Report (AR4), characterizing multidecadal variability in North
Atlantic sea surface temperatures in comparison with modern and paleoclimate reconstructions
of the AMO.
1. Introduction
The Atlantic Multidecadal Oscillation (AMO) [Kerr, 2000] is the leading mode of variability in
global sea surface temperatures (SST), excluding the El Niño-Southern Oscillation (ENSO)
[Mestas-Nuñez and Enfield, 1999]. Understanding and predicting the AMO is critical for
disentangling trends in global temperatures due to anthropogenic aerosol and greenhouse gas
forcings from variability in the ocean circulation on comparable timescales [Gordon et al., 1992;
Houghton et al., 2001]. Understanding the AMO is also important for regional climate prediction
throughout the Atlantic basin, because of the strong association between the AMO and extreme
weather and climate events such as hurricanes and droughts, [e.g., Goldenberg et al., 2001;
Enfield et al., 2001].
While Trenberth and Shea [2006] argue that the AMO is an oceanic response to the
anthropogenic forcings of greenhouse gases and aerosols, Enfield and Cid-Serrano [2009] show
that a secular trend and multidecadal variability in North Atlantic SST can be easily
distinguished in the modern instrumental record and that multidecadal North Atlantic SST
variability is consistent with paleo-proxy series such as those of Delworth and Mann [2000] and
Gray et al. [2004], which extend well before the 20th century, the period of greatest
anthropogenic forcing. Thus, the AMO seems to be an intrinsic component of the Earth’s nonanthropogenically-forced climate system.
The AMO cannot be easily investigated using the instrumental record, because its period
is about three times the length of the instrumental SST record and may involve processes at
depth that cannot be observed as easily as SST. So climate models that simulate multidecadal
North Atlantic variability [e.g., Delworth et al., 1993; Timmermann et al., 1998; Delworth and
Greatbatch, 2000; Dong and Sutton, 2005; Dijkstra et al., 2006; Latif et al., 2006; Msadek and
Frankignoul, 2009] can be used to understand its dynamics and to validate dynamical theory
against the limited observational record.
In this study, we perform a simple intercomparison of pre-industrial control experiment
runs generated for the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment
Report (AR4) from the World Climate Research Programme's (WCRP's) Coupled Model
Intercomparison Project phase 3 (CMIP3) multi-model dataset, focusing on the simulation of
multidecadal North Atlantic SST variability. These model runs have four advantages: (1) they
are publicly available; (2) they are output from the models that are used now for climate
prediction; (3) they have a standard format; (4) and they have a constant greenhouse gas and
aerosol forcing, so that oscillations in them cannot be attributed to variable forcing. We
determine which CMIP3 models simulate multidecadal variability in North Atlantic SST that
resembles the AMO without secular forcing. This study is thus both a broad assessment of the
state of global climate models in representing the AMO as a natural, internal mode of variability
(serving as a check on simulation of the anthropogenically-forced system) and a means of
identifying global climate models appropriate for a broad ensemble study of AMO dynamics.
2. Methods
The longest pre-industrial control experiment run for the highest spatial resolution
configuration available was analyzed for each CMIP3 model. In cases in which multiple preindustrial control experiment runs of the same length were available, only the run with both
monthly and daily data was analyzed in order to ensure higher temporal resolution
characterization of air-sea fluxes would be possible if necessary. A list of the models and runs
analyzed is given in Table 1. Note that two models runs are only 100 years in duration, so we are
unable to assess centennial-scale variability within them. The global SST drift in each model
was assessed by calculating the deseasonalized area-weighted SST anomaly for all ocean
gridboxes in the model run. Most drift trends appear linear by inspection, so the trend was fit
with a linear equation and the slope was taken to be the rate of drift.
The AMO Index was calculated as a monthly series for each model run. Here, the AMO
Index is defined as the deseasonalized and detrended area-weighted SST anomaly for the North
Atlantic Ocean (0º-75º N, 285º-350º E, gridboxes with >99% ocean). The northern boundary of
the box is slightly more poleward than used by Enfield et al. [2001] in order to compensate for
wide grid resolution near the pole in some models. A couple of months missing from output of
the HadCM3 model were filled by linear interpolation between the values in the prior and
succeeding month.
An estimate of the amplitude of the AMO was derived from the variance, 2, of the tenyear running mean of the AMO Index (defined by Enfield et al. [2001] as the AMO Index). In
the case of a simple sinusoid, the amplitude, A, is 2 2 [Smith, 1997]. We treat the ten-year
running mean of the AMO Index like a simple sinusoid, but inspection suggests this is only an
approximation.

A global spatial pattern for North Atlantic SST variability in each model run was
generated by finding the Pearson’s coefficient of correlation between the annual mean AMO
Index and the deseasonalized and detrended SST anomaly time series for each grid point in the
model.
The power spectrum of the calculated AMO Index for each model run was calculated and
compared with red noise spectra and confidence level spectra for 90%, 95%, and 99%. To
suppress any annual signal after deseasonalization, the power spectrum analysis is performed on
the annual mean AMO Index.
For comparison with the paleoclimate record, amplitudes and power spectra also were
calculated for the reconstructed annual mean North Atlantic SST anomaly series of Gray et al.
[2004] provided by the World Data Center for Paleoclimatology (Boulder, CO). For comparison
with the modern instrumental record, the AMO Index, amplitude, power spectrum, and North
Atlantic SST spatial pattern were calculated from the HadISST (v1.1) dataset (1870-2008)
[Rayner et al., 2003]. The reconstructed AMO Index of Gray et al. [2004] only correlates with
the AMO Index, so the amplitude of the reconstructed AMO during the instrumental record
period is a factor of 5 higher than the amplitude of the instrumental record-based AMO Index.
Thus, we scale the reconstructed AMO Index by the instrumental AMO Index over the period of
overlap to obtain an amplitude for the pre-industrial AMO.
3. Results
Figure 1 shows the calculated spatial patterns of North Atlantic SST variability. The HadISST
record pattern (Figure 1a) shows strong positive correlation of the AMO Index with both
extratropical and tropical North Atlantic SST (but not with South Atlantic SST) and weaker
correlation near the East Coast of the United States, possibly corresponding to the region
occupied by the Gulf Stream. While most model runs have patterns of North Atlantic SST
variability roughly consistent with this pattern, the analyzed model runs typically overestimate
the extent of this region of poor correlation and even simulate SSTs in this region anticorrelating with the average North Atlantic SST. The model with the closest pattern to the
observed pattern may be: GISS-AOM (Figure 1i).
Figure 2 shows the power spectra of the model runs and comparison datasets scaled by
the calculated red noise spectra to emphasize statistically significant peaks. Note that North
Atlantic SST in the pre-industrial era varied with periods of ~65 and ~160 years but varies with a
period of ~37 years in the modern instrumental record. Power spectrum analysis of the Gray et
al. [2004] reconstructed AMO Index series over the period of the modern instrumental record
also has a significant period at 37 years, implying that the pre-industrial period of the AMO may
have differed from the modern period. However, significant variability at these timescales is
absent in most model runs, which generally have either decadal to bi-decadal variability or
greater than centennial variability. The exceptions are BCCR-BCM3.0 (Figure 2c) with a period
of variability near the modern; GFDL CM2.1 (Figure 2i) with a period of ~65 years and some bidecadal variability; GISS AOM (Figure 2j) with a period of ~ 65 years and some bi-decadal
variability; ECHO-G (Figure 2r) with periods of variability at ~70 and ~160 years, a power
spectrum that very strongly resembles the power spectrum of the Gray et al. [2004] record; MRI
CGCM 2.3.2 (Figure 2t) with a broad multi-decadal to centennial and bi-decadal periods of
variability; PCM1 (Figure 2v) with multiple multi-decadal periods of variability; and UKMOHadCM3 (Figure 2w) with a broad multi-decadal peak.
The amplitudes of the model AMOs vary about an order of magnitude from 0.1658 K in
GISS-ER (the non-detrended amplitude is similar) to 1.54 K in FGOALS-g1.0. If FGOALS-g1.0
is excluded, most models have amplitudes on the order of those estimated from the paleoclimate
and modern instrumental records. Note that the AMO amplitude in the ECHO-G run is within
4% of that we inferred from the Gray et al. [2004] reconstruction.
4. Summary
7 of 22 CMIP3 models under pre-industrial forcing conditions simulate North Atlantic
multi-decadal SST variability and have spatial patterns of North Atlantic SST variability during
the instrumental era and have Only one of these models, ECHO-G, reproduces the limited
variability at shorter periods and centennial-scale variability seen in the pre-industrial AMO,
which may add uncertainty to predictions of future climate that use the full CMIP3 model
ensemble or models with the same heritage. A study by Stoner et al. [2009] recently considered
the simulation of a variety of teleconnection patterns (including the AMO) by the CMIP3
simulations of Twentieth-Century Climate in Coupled Models, but the relevant ECHO-G run
was not analyzed, so we do not know if the model generally simulates an AMO. While previous
studies have emphasized the importance of determining whether the AMO is purely oceanic or
coupled atmosphere-ocean mode, we propose that the results of this study underscore the greater
importance of investigating what determines the frequency characteristics of the AMO. The
model runs studied here may constitute a suitable ensemble for such an investigation.
Acknowledgments
References
Delworth T.L. and R.J. Greatbatch (2000) Multidecadal thermohaline circulation variability
driven by atmospheric surface flux forcing, J. Climate, 13, 1481–1495.
Delworth, T. L., and M. E. Mann (2000), Observed and simulated multidecadal variability in the
Northern Hemisphere, Climate Dyn., 16, 661-676.
Delworth T.L, Manabe S., Stouffer R.J. (1993) Interdecadal variations of the thermohaline
circulation in a coupled ocean–atmosphere model. J. Climate, 6, 1993–2011.
Dijkstra, H.A., L. te Raa, M. Scmeits, and J. Gerrits (2006), Ocean Dynamics, 56, 36-50, doi:
10.1007/s10236-005-0043-0.
Dong B. and R.T. Sutton (2005), Mechanism of interdecadal thermohaline
circulation variability in a coupled ocean–atmosphere gcm, J. Climate, 18, 1117–1135.
Enfield, D. B., A. M. Mestas-Nuñez, and P. J. Trimble (2001), The Atlantic multidecadal
oscillation and its relation to rainfall and river flows in the continental US, Geophys. Res. Lett,
28, 2077– 2080.
Enfield, D.B. and L. Cid-Serrano (2009), Secular and multidecadal warmings in the North
Atlantic and their relationships with major hurricane activity, Int. J. Climat., in press,
doi:10.1002/joc.1881.
Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nuñez, and W. M. Gray (2001), The recent
increase in Atlantic hurricane activity: Causes and implications, Science, 293, 474–479.
Gordon, A.L., S.E. Zebiak, and K. Bryan, Climate Variability and the Atlantic Ocean, Eos, 73
(15), 161-176.
Gray, S.T., L.J. Graumlich, J.L. Betancourt, and G.T. Pederson (2004), A tree-ring based
reconstruction of the Atlantic Multidecadal Oscillation since 1567 A.D.
Geophysical Research Letters, 31, L12205, doi:10.1029/2004GL019932.
Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, and D. Xiaosu, Eds.,
(2001), Climate Change 2001: The Scientific Basis: Contributions of Working Group I to the
Third Assessment Report of the Intergovernmental Panel on Climate Change, 881 pp.,
Cambridge University Press, Cambridge, UK.
Kerr, R.A. (2000), A North Atlantic Climate Pacemaker for the Centuries, Science, 288 (5473),
1984-1985.
Latif M., C. Böhning, J. Willebrand, A. Biastoch, J. Dengg, N. Keenlyside,U. Schweckendiek
(2006), Is the thermohaline circulation changing?, J Climate, 18, 4631–4637.
Meehl, G.A, L. Goddard, J. Murphy, R.J. Stouffer, G. Boer, G. Danabasoglu, K. Dixon,
M.A. Giorgetta, A.M. Greene, E. Hawkins, G. Hegerl, D. Karoly, N. Keenlyside, M.
Kimoto, B. Kirtman, A. Navarra, R. Pulwarty, D. Smith, D. Stammer, and T. Stockdale
(2009), Decadal Prediction, Bull. Amer. Met. Soc., 90 (10), 1467-1485.
Mestas-Nuñez, A.M. and D.B. Enfield (1999), Rotated global modes of non-ENSO sea surface
temperature variability. J. Climate, 12, 2734-2746.
Msadek, R. and C. Frankignoul (2009), Atlantic multidecadal oceanic variability and its
influence on the atmosphere in a climate model, Climate Dyn., 33, 45-62.
Nye, J.A., J.S. Link, J.A. Hare, and W.J. Overholtz (2009), Changing spatial distribution of fish
stocks in relation to climate and population size on the Northeast United States continental shelf,
Mar. Ecol. Prog. Ser., 393, 111-129, doi:10.3354/meps08220.
Santer, B.D., T.M.L. Wigley, P.J. Gleckler, C. Bonfils, M.F. Wehner, K. AchutaRao, T.P.
Barnett, J.S. Boyle, W. Brüggemann, M. Fiorino, N. Gillett, J.E. Hansen, P.D. Jones, S.A. Klein,
G.A. Meehl, S.C.B. Raper, R.W. Reynolds, K.E. Taylor, and W.M. Washington (2006), Forced
and unforced ocean temperature changes in Atlantic and Pacific tropical cyclogenesis region,
Proc. Natl. Acad. Sci. U.S.A., 103 (38), 13905-13910, doi:10.1073/pnas.0602861103.
Smith, S.W. (1997), The Scientist’s and Engineer’s Guide to Digital Signal Processing, 626 pp.,
California Technical Publishing, San Diego.
Timmermann A, M. Latif, R. Voss, A. Grotzner (1998) Northern hemispheric interdecadal
variability: a coupled air–sea mode, J. Climate, 11, 1906–1931
Trenberth, K. E., and D. J. Shea (2006), Atlantic hurricanes and natural variability in 2005,
Geophys. Res. Lett., 33, L12704, doi:10.1029/2006GL026894.
Tables
Table 1: Global climate model runs from the CMIP3 database analyzed in this study and global
SST drift in these model runs
(see attached .pdf file)
Figures
Figure 1: Spatial patterns associated with North Atlantic SST variability derived from the
Pearson’s correlation coefficient between the annual mean AMO Index and the annual mean
detrended and deseasonalized series of SST at each point. The thick black line indicates zero
correlation. (a) HadISST (instrumental record); (b) BCCR BCM2.0; (c) CGCM3.1(T47); (d)
CNRM CM3; (e) CSIRO Mk3.0; (f) CSIRO Mk3.5; (g) GFDL-CM 2.0; (h) GFDL-CM 2.1; (i)
GISS-AOM; (j) GISS-EH (k) GISS-ER (l) FGOALS-g1.0; (m) ECHAM4; (n) INM CM3.0 (o)
IPSL-CM4; (p) MIROC 3.2 HIRES; (q) ECHO G; (r) ECHAM5/MPI OM; (s) MRI CGCM2.3.2;
(t) CCSM3; (u) PCM1; (v) UKMO-HadCM3;(w) UKMO-HadGEM1.
Figure 2: Power spectrum of the annual mean AMO Index scaled by the red noise spectrum. The
red dashed and red solid lines mark the 95% and 99% confidence levels respectively: (a) Gray et
al. [2004] reconstruction of the AMO Index, 1567-1870; (b) HadiSST (instrumental record); (c)
BCCR BCM2.0; (d) CGCM3.1(T47); (e) CNRM CM3; (f) CSIRO Mk3.0; (g) CSIRO Mk3.5;
(h) GFDL-CM 2.0; (i) GFDL-CM 2.1; (j) GISS-AOM; (k) GISS-EH (l) GISS-ER (m)
FGOALS-g1.0; (n) ECHAM4; (o) INM CM3.0 (p) IPSL-CM4; (q) MIROC 3.2 HIRES; (r)
ECHO G; (s) ECHAM5/MPI OM; (t) MRI CGCM2.3.2; (u) CCSM3; (v) PCM1; (w) UKMOHadCM3;(x) UKMO-HadGEM1.
Figure 3: Amplitude of the AMO Index (K): (a) Gray et al. [2004] reconstruction of the AMO
Index, 1567-1870; (b) HadiSST (instrumental record); (c) BCCR BCM2.0; (d) CGCM3.1(T47);
(e) CNRM CM3; (f) CSIRO Mk3.0; (g) CSIRO Mk3.5; (h) GFDL-CM 2.0; (i) GFDL-CM 2.1;
(j) GISS-AOM; (k) GISS-EH (l) GISS-ER (m) FGOALS-g1.0; (n) ECHAM4; (o) INM CM3.0
(p) IPSL-CM4; (q) MIROC 3.2 HIRES; (r) ECHO G; (s) ECHAM5/MPI OM; (t) MRI
CGCM2.3.2; (u) CCSM3; (v) PCM1; (w) UKMO-HadCM3;(x) UKMO-HadGEM1.