AUXILIARY MATERIAL
Stationarity of the Tropical Pacific Teleconnection to North America in CMIP5/PMIP3
Model Simulations
Sloan Coats1, Jason E. Smerdon1, Benjamin I. Cook2, and Richard Seager1
1
LDEO, New York, USA; 2GISS, New York, USA
ADDITIONAL DISCUSSION OF METHODS
I. Signal-to-noise analysis of SST forcing on NA teleconnection
Our main analysis demonstrates multidecadal teleconnection variability across a wide
range of models and simulations and concludes that the majority of the variability is associated
with changes in tropical Pacific SSTs. This conclusion was made by comparing an ensemble of
atmosphere only simulations to coupled simulations, with fully dynamic SSTs, over the period
1979-2005. While the results clearly indicate a much larger role for ocean dynamics in
determining the character of the NA teleconnection, it is worth considering the potential for our
analysis to overestimate the contribution of the SST boundary forcing relative to that from
internal atmospheric variability. This is particularly important given the strong connection
between a model’s inherent tropical Pacific SST variability and the stationarity of its
teleconnection as demonstrated in Figure 3C of the main paper. While we have strongly
concluded that this represents the ability of ENSO to rise above noise from secondary patterns of
coupled ocean-atmosphere variability, large SST variance in the tropical Pacific will also allow
the signal of ENSO to rise above any noise from internal atmospheric variability. Given this, and
the difficulty with controlling certain aspects of the SST forced signal to internal atmospheric
analysis (mostly due to the limited ensemble size), we will revisit potential sources of error
herein.
Firstly, the AMIP ensemble will simulate a realistic teleconnection over NA with a higher
probability than the coupled model simulations. That is, relative to the AMIP ensemble, the
inter-model ensemble of coupled runs is expected to have a more non-stationary teleconnection
simply because different coupled models will have different levels of interannual SST
variability. For example a model with very small SST variability, might be expected to have a
poor teleconnection because it is always disrupted by internal atmospheric variability. The
correlation in Figure S1 does indeed indicate a weak connection between the amplitude of the
Niño3.4 index and the CPCS in the historical simulations but this is not statistically significant at
the 95% level using a two-tailed test with a white noise null-hypothesis.
Figure S2 further indicates that an AMIP model's level of total atmospheric variability
cannot account for inter-model differences in CPCS range. It might be expected that a model
with a high level of total atmospheric variability would be one in which the CPCS range was
large because the SST forced component was competing against a high level of internal
variability. A high level of total atmospheric variability, however, could also arise from a strong
SST-forced component. The relative contributions of SST-forced and internal atmospheric
variability could only be determined with large ensembles for each model that are not available
here. Furthermore, the strength and character of the teleconnection to NA fundamentally
depends on the interaction between the SST-forced Rossby wave and the North Pacific storm
track [Hoerling and Ting, 1994; Seager et al., 2010]. Given these considerations it is perhaps not
surprising that there is no clear and strong relationship between the CPCS range and the level of
total atmospheric variability. These results further support our interpretations in the main paper
that the impact of large-amplitude tropical Pacific SST variability on the NA teleconnection is
associated with the ability of ENSO to emerge above other patterns of coupled variability.
As a final consideration, the strength of the teleconnection, as measured by the root mean
square (RMS) of the regression coefficients over NA from the regression of the Niño3.4 index
against the 200mb geopotential height field (both DJF averages), is plotted against the
teleconnection stationarity in Figure S3. There is a clear relationship between teleconnection
strength and stationarity, implying that stationarity comes from strong teleconnections rising
above noise from other sources of variability. This relation is nevertheless weaker than that
between a model’s inherent tropical Pacific SST variability and the teleconnection stationarity.
This provides further evidence that large variability in the tropical Pacific, and more generally
ocean dynamics, are an important control on the simulated teleconnection.
References
Hoerling, M. P., and M. Ting (1994), Organization of extratropical transients during El Niño, J.
Climate, 7, 745-766.
Seager, R., N. Naik, M. A. Cane, N. Harnik, M. Ting, and Y. Kushnir (2010), Adjustment of the
atmospheric circulation to tropical Pacific SST anomalies: Variability of transient eddy
propagation in the Pacific-North America sector, Quart. J. Roy. Meteor. Soc., 136, 277-296.
Table 1: Model information for the analyzed CMIP5 simulations.
Modeling Center
Commonwealth Scientific and Industrial
Research Organization (CSIRO) and
Bureau of Meteorology (BOM), Australia
Beijing Climate Center, China
Meteorological Administration
Canadian Centre for Climate Modelling
Analysis
National Center for Atmospheric
Research
Center National de Recherches
Météorologiques/Centre de Recherche
et Formation Avancée Calcul
Scientifique
Commonwealth Scientific and Industrial
Research Organization in collaboration
with Queensland Climate Change Centre
of Excellence
First Institute of Oceanography, SOA,
China
LASG, Institute of Atmospheric Physics,
Chinese Academy of Sciences
NOAA Geophysical Fluid Dynamics
Laboratory
Institute ID
CSIRO-BOM
Model Name
ACCESS1.3
BCC
BCC-CSM1.1
CCMA
CanEMS2, CanAM4
NCAR
CCSM4
CNRM-CERFACS
CNRM-CM5
CSIRO-QCCE
CSIRO-Mk3.6.0
FIO
FIO-ESM
LASG-IAP
FGOALS-gl
NOAA GFDL
NASA Goddard Institute for Space
Studies
Institute for Numerical Mathematics
Institute Pierre-Simon Laplace
Japan Agency for Marine-Earth Science
and Technology, Atmosphere and Ocean
Research Institute (The University of
Tokyo), and National Institute for
Environmental Studies
Max-Planck-Intitut für Meteorologie
(Max Planck Institute for Meteorology)
Meteorological Research Institute
NASA GISS
GFDL-ESM2G, GFDLESM-2M, GFDL-HIRAMC180
GISS-E2-R
INM
IPSL
MIROC
INM-CM4
IPSL-CM5A-LR
MIROC-ESM
MPI-M
MPI-ESM-LR, MPI-ESM-P
MRI
Norwegian Climate Centre
NCC
MRI-AGCM2-2H, MRICGCM3
NorESM1-M
Figure S1: The average interannual variance of the DJF Niño3.4 anomaly plotted against the
teleconnection CPCS value for each historical simulation. The correlation and P-value (twotailed) are given in the figure inset.
Figure S2: The average interannual variance of the 200mb DJF geopotential height anomaly
over NA plotted against the average value of the CPCS for each AMIP model. The correlation
and P-value (two tailed) are given in the figure inset.
Figure S3: Teleconnection strength as measured by the RMS over NA of the coefficients
from the linear regression of the DJF Niño3.4 index against the 200mb DJF geopotential
height anomaly over NA for the control and LM simulations plotted against the
teleconnection CPCS range. The correlation and P-value (two tailed) are given in the figure
inset.