Climate Dynamics (2006) 26: 229–245
DOI 10.1007/s00382-005-0087-3
Marika M. Holland Æ Marilyn N. Raphael
Twentieth century simulation of the southern hemisphere climate
in coupled models. Part II: sea ice conditions and variability
Received: 26 May 2005 / Accepted: 25 September 2005 / Published online: 3 December 2005
Ó Springer-Verlag 2005
Abstract We examine the representation of the mean
state and interannual variability of Antarctic sea ice in
six simulations of the twentieth century from coupled
models participating in the Intergovernmental Panel on
Climate Change fourth assessment report. The simulations exhibit a largely seasonal southern hemisphere ice
cover, as observed. There is a considerable scatter in the
monthly simulated climatological ice extent among different models, but no consistent bias when compared to
observations. The scatter in maximum winter ice extent
among different models is correlated to the strength of
the climatological zonal winds suggesting that wind
forced ice transport is responsible for much of this
scatter. Observations show that the leading mode of
southern hemisphere ice variability exhibits a dipole
structure with anomalies of one sign in the Atlantic
sector associated with anomalies of the opposite sign in
the Pacific sector. The observed ice anomalies also exhibit eastward propagation with the Antarctic circumpolar current, as part of the documented Antarctic
circumpolar wave phenomenon. Many of the models do
simulate dipole-like behavior in sea ice anomalies as the
leading mode of ice variability, but there is a large discrepancy in the eastward propagation of these anomalies
among the different models. Consistent with observations, the simulated Antarctic dipole-like variations in
the ice cover are led by sea-level pressure anomalies in
M. M. Holland (&)
National Center for Atmospheric Research,
PO Box 3000, Boulder, CO, 80307 USA
E-mail: [email protected]
Tel.: +1-303-4971734
Fax: +1-303-4971700
M. N. Raphael
UCLA Department of Geography, 1255 Bunche Hall,
Los Angeles, CA, 90095-1524 USA
E-mail: [email protected]
Tel.: +1-310-2064590
Fax: +1-310-2065976
the Amundsen/ Bellingshausen Sea. These are associated, to different degrees in different models, with both
the southern annular mode and the El Nino-Southern
Oscillation (ENSO). There are indications that the
magnitude of the influence of ENSO on the southern
hemisphere ice cover is related to the strength of ENSO
events simulated by the different models.
1 Introduction
Sea ice is important in that it influences the surface energy budget and redistributes fresh water in the ice–
ocean system. Feedbacks associated with the sea ice
cover, such as the influence of ice conditions on surface
albedo changes, make it a particularly sensitive component of the climate system. As such, simulated conditions at high latitudes, including the mean state,
variability and change, are strongly dependent on the
state of the sea ice cover. Because of this, it is important
to assess both the mean state and variability of the sea
ice cover in coupled model simulations. Comparing
simulated variability in the sea ice to observations gives
some indication of the realism of the simulated sea ice
sensitivity and climate processes that influence the ice
cover. A better understanding of these issues has
important consequences for our ability to simulate future high-latitude climate conditions.
In contrast to the northern hemisphere where large
recent changes in the sea ice have been observed (e.g.,
Rothrock et al. 1999; Serreze et al. 2003; Stroeve et al.
2005), the southern hemisphere ice cover has only
modest trends (e.g., Zwally et al. 2002). Additionally,
many model solutions (e.g. IPCC 2001) suggest that
future trends in the southern hemisphere sea ice are
smaller than those in the north. This is related to differences in ocean heat uptake in the high northern and
southern latitudes. However, there is a large range in the
simulated warming at high southern latitudes among
different models (e.g., IPCC 2001) and some studies
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Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
suggest that improvements in model physics can reduce
the southern ocean heat uptake (Flato and Boer 2001).
This indicates that uncertainties in the southern highlatitude sea ice and ocean response to climate perturbations are considerable.
The southern hemisphere ice cover is largely seasonal.
The leading mode of ice variability exhibits anomalies of
opposite sign in the Atlantic and Pacific sectors (e.g.,
Yuan and Martinson 2000). This is associated with both
atmosphere and ocean conditions in these regions and this
coupled variability has been referred to as the ‘‘Antarctic
Dipole’’ (ADP) by Yuan and Martinson (2000, 2001). A
distinct, although likely related, phenomenon is the
Antarctic circumpolar wave (ACW) that has been documented by White and Peterson (1996). The ACW is a
zonal wave number-2 pattern of covarying anomalies in
sea level pressure (SLP), sea surface temperature, ice extent anomalies, and sea surface height anomalies (White
and Peterson 1996; Jacobs and Mitchell 1996). These
anomalies propagate eastward and encircle the Antarctic
continent in approximately 8–10 years. However, there
are indications that the ACW is not continuously present
in the observed timeseries (e.g., Connolley 2003), but that
other spatial structures (wave-3) are more dominant
during portions of the observed record. Individual climate
models have shown some ability to simulate aspects of
ACW (e.g., Christoph et al. 1998; Cai et al. 1999; Haarsma et al. 2000) or ADP (e.g., Liu et al. 2002; Holland
et al. 2005) like variability. These studies have provided
important insight into the possible mechanisms driving
southern hemisphere sea ice variability.
In order to diagnose the Antarctic sea ice representation in climate of the twentieth century simulations we
have examined both the mean properties of the ice cover
and interannual variability in ice concentration. The
focus is on ice conditions from the latter part of the
twentieth century, specifically from 1960–1999. This allows for a comparison to observations (available from
1979 to present) and provides consistency with the discussion of simulated atmospheric variability in Part I of
this study (Raphael and Holland, this issue). We
examine the interannual variability in six coupled climate models that are participating in the Intergovernmental Panel on Climate Change fourth assessment
report (IPCC-AR4). These include the CCSM3, CSIROMk3.0, GFDL-CM2.1, GISS-ER, MIROC3.2(hires),
and UKMO-HadCM3. The mean ice cover, the ice extent annual cycle, and the leading modes of sea ice
variability are presented. A discussion of the forcing of
the ice anomalies, including the influence of the Southern annular mode (SAM) and the El Nino-Southern
Oscillation (ENSO) on the southern hemisphere ice
variability is also given.
2 Model simulations
We examine the sea ice conditions from a number of
integrations of the twentieth century. These simulations
were typically initialized from a pre-industrial control
run of varying lengths (where the year defined as ‘‘preindustrial’’ varied among the different models) and then
integrated through the twentieth century using observed
changes in a number of anthropogenic and natural
forcings. These included changes in greenhouse gases,
volcanic forcing, ozone, and solar input, although different models used different subsets of these forcings
(e.g. see http://www-pcmdi.llnl.gov).
Simulations from six different fully coupled climate
models were used for our analysis. In particular, we
examine model output from the CCSM3 (Collins et al.
2005), CSIRO-Mk3.0 (Gordon et al. 2002), GFDLCM2.1 (Delworth et al. 2005), GISS-ER (Schmidt et al.
2005), MIROC3.2(hires) (K-1 model developers 2004),
and UKMO-HadCM3 (Gordon et al. 2000). Information about these models is listed in Raphael and Holland
(this issue, Part I) and they are all participating in the
Intergovernmental Panel on Climate Change fourth
assessment report (IPCC-AR4). The sea ice components
of these coupled systems are all dynamic/thermodynamic models. However, the complexity of the
‘‘dynamics’’ varies, with all of the models using some
representation of ice rheology except for the UKMOHadCM3 which uses a simple free-drift approximation.
More information about these models is given in the
relevant references.
For some of these models, multiple ensembles of the
twentieth century integrations were available for analysis. However, the ensemble size varies for the different
models and we are interested in the natural interannual
variability in the ice cover, so for this study we have
focused on only a single ensemble member from each
model. This provides a more clean comparison among
different models and to the observed record, which of
course has only a single realization. In a statistical sense,
the different ensemble members for an individual model
are usually quite similar. The exception to this is the
GFDL-CM2.1 runs. All of the ensemble members for
this run have considerable low frequency variability in
the Weddell Sea ice cover over the twentieth century.
However, they are influenced by this variability to different degrees, with runs that were initialized from earlier in the preindustrial control simulation having
dominant variability in this region. This suggests that
these variations may be due to model initialization. As
such, we use the run that was initialized from the preindustrial control simulation at year 81 (the latest year
available) for our analysis.
3 Mean conditions
The annual cycle averaged from 1980 to 1999 of total ice
extent in the southern hemisphere (defined to be the area
of ice with a concentration greater than 15%) is shown
in Fig. 1. All the models capture some important features of the southern hemisphere ice pack. They all have
a large annual cycle with an Antarctic sea ice cover that
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
Fig. 1 The annual cycle of southern hemisphere ice extent defined
to be the area of ice with concentrations greater than 15%
is largely seasonal. There is no consistent ice extent bias
in the models for the monthly climatology, but considerable scatter among them. A comparison of the maps of
simulated September and March ice concentration
(Fig. 2) with the observed concentration from HadISST
data (Rayner et al. 2003)(Fig. 3) shows that many of the
models have compensating biases in different regions.
The exception to this is the CCSM3 model which typically has too much winter ice cover in all regions.
There is no obvious relationship among the biases in
the simulated climatological ice cover and sea ice model
resolution or physical processes. However, an analysis of
the simulated zonally averaged SLP suggests that the
scatter in the maximum wintertime ice extent among
different model is related to the simulated zonal winds.
Figure 4 shows a scatter plot of the maximum winter ice
extent versus the gradient in the zonally averaged April–
June SLP at 60S. The two are significantly correlated at
R=0.78. The gradient of the zonally averaged SLP gives
an indication of the strength of the zonal geostrophic
winds. As westerly winds drive an equatorward Ekman
sea ice transport, which is rapidly replaced with newly
formed sea ice near the continent, larger ice extent is
simulated by models with stronger westerly winds. This
relationship is strongest for SLP conditions during the
ice growth season (April–June) when the ice edge is
rapidly expanding. Other aspects of the model simulations that affect ice growth rates, such as the radiative
fluxes or ocean heat transport, also likely contribute to
the simulated ice extent biases.
4 Sea ice variability
4.1 Twentieth century timeseries
The observed timeseries of southern hemisphere sea ice
conditions is relatively short with satellite observations
231
available from 1979 to present. Over this time period,
there is a small increasing trend in the total annual
averaged ice extent (e.g., Zwally et al. 2002). This is in
part due to compensating trends in different regions,
with the Bellingshausen–Amundsen Seas exhibiting
decreasing ice cover and most other regions experiencing
an increase. Because of the short nature of the timeseries, it is difficult to ascertain if these observed trends are
part of the natural variability in the system or are being
forced by, for example, anthropogenic greenhouse gas
changes or stratospheric ozone changes.
The timeseries of simulated twentieth century annual
average southern hemisphere ice extent anomalies is
shown in Fig. 5. The September average values exhibit a
similar timeseries but with generally larger magnitude
anomalies, particularly for the models which have low
summer ice cover (e.g. GFDL_CM2.1 and GISS-ER).
For consistency with the available observations, the
anomalies are taken relative to the 1980–2000 mean
values. Many of the model runs discussed here may have
model spin-up trends which could influence the ice extent timeseries. We have considered this by using a
comparison with the pre-industrial control simulations
for the equivalent simulation years. These spin-up trends
do not appear to have a considerable influence on the
average timeseries results discussed here, although they
have some influence on the spatial patterns of variability
as discussed below (Sect. 4). As such, the analysis shown
uses the raw model output from the twentieth century
integrations.
While the observations show increases in the southern hemisphere ice cover from 1979 to 2000, this is only
seen in the simulated timeseries on Fig. 5 from CSIRO_Mk3.0 and GFDL_CM2.1. However, an analysis of
other ensemble members from the model simulations
discussed here reveals that some members from other
models (not shown) do have increasing southern hemisphere ice cover from 1979 to 2000 and that the other
GFDL_CM2.1 ensemble members have a decreasing ice
trend over this period. The limited number of ensemble
members from different models (for example MIROC3.2(hires) only has a single member) makes it difficult to provide a robust assessment of how likely an
increasing trend is in different models. However, the fact
that the sign of this trend varies among different
ensemble members for at least some models, does suggest that the observed trend may not be an externally
forced trend. As there is no reason to expect that the
natural variations in the real world and model simulations will coincide in time, it does not necessarily reflect
poorly on the model if it does not show an increasing
trend at the end of the twentieth century.
If the statistical signature of the simulated and observed timeseries are considerably different, it may
indicate a bias in the model simulation. The most
notable difference in this respect between the simulated
and observed timeseries is the considerably larger variance which is present in the model simulations. This is
shown more quantitatively in an analysis of the 20-year
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Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
Fig. 2 The September (annual
maximum) ice concentration
averaged from 1979 to 1999
from the model runs. The
contour interval is 10%. The
thick contour shows the 10%
March (annual minimum)
average ice concentration.
Shown are the results from a
CCSM3, b CSIRO-Mk3.0, c
GFDL-CM2.1, d GISS-ER, e
MIROC3.2(hires), and f
UKMO-HadCM3
running standard deviation of the southern hemisphere
annual average ice extent (Fig. 6). In general, the simulated 20-year standard deviation is from 20% to
approximately 200% larger than the observed value.
Any single model can have a considerable range in this
20-year standard deviation depending on the time period, indicating the lower frequency variability in the
simulated southern hemisphere sea ice. However, for all
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
Fig. 3 The observed September (annual maximum) ice concentration averaged from 1979 to 1999 from the HadISST data. The
contour interval is 10%. The thick contour shows the 10% March
(annual minimum) average ice concentration
233
could have implications for sea ice forced changes in
ocean and atmosphere conditions such as the variable
ocean buoyancy forcing that results from ice growth rate
changes.
An analysis of the ice extent standard deviation for
different months reveals that all of the models overestimate the winter ice variability; There is no consistent
bias in the summer ice variability however, with the
GFDL_CM2.1 having almost no summer ice extent
variability and the CCSM3 having anomalously large
summer ice variability. This is consistent with the climatological summer ice cover in these models as shown
in Fig. 2.
From an analysis of the ice extent variability in different regions (not shown), it is not obvious that a
particular region is responsible for the excessive total
southern hemisphere annual average ice extent variability in the simulations. Many of the models do a
reasonable job in simulated variance in certain regions
and even underestimate it in certain locations (particularly the Ross Sea). It is possible that the observed ice
cover has regions of compensating anomalies that are
absent or reduced in the simulations. This could lead to
excessive total ice extent variability even though individual regions may be quite reasonable. An analysis of
the observations shows a negative correlation
(R= 0.56) between winter ice anomalies in the Atlantic
and the Pacific sectors. Simulations which exhibit a
negative correlation between these regions do tend to
have a smaller and more realistic total southern hemisphere ice extent standard deviation, indicating that the
compensation between different regions is important for
simulating the variability of the total southern hemisphere ice extent.
The leading mode of variability from the observations exhibits these compensating anomalies in the Pacific and Atlantic sectors of the southern ocean (e.g.,
Yuan and Martinson 2000). This pattern has been referred to as the ADP. We now examine this leading
mode of variability in more detail.
4.2 Modes of sea ice variability
Fig. 4 A scatter plot of the April–June zonally-averaged SLP
gradient at 60S versus the maximum southern hemisphere ice
extent for the observations and different models
the model runs and almost all 20-year segments of the
twentieth century, the models have larger ice extent
variance than the observations. The only exception to
this is the CSIRO-Mk3.0 simulation, which does generally have larger ice extent variance than the observations except from approximately 1930 to 1950 where it
has a somewhat lower ice extent variance. Overall, this
suggests that all of the model simulations are overestimating the annual averaged ice extent variability. This
To examine the modes of variability in the southern
hemisphere ice cover, an empirical orthogonal function
(EOF) analysis has been performed on the ice concentration. We focus here on an analysis performed on the
winter (JAS) mean values since only minimal ice cover is
present during the summer months. The results are not
particularly sensitive to the exact averaging period that
is used, and for example quite similar results are obtained using September averaged values.
Figure 7 shows the first two EOFs from the observations computed from 1979 to 1999 when satellite data
are available. The first EOF accounts for approximately
29% of the variance in the ice concentration and shows a
‘‘dipole’’ like pattern with anomalies of one sign over
much of the Pacific and anomalies of opposite sign in the
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Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
Fig. 5 The timeseries of annual
average twentieth century
southern hemisphere ice extent
anomalies taken relative to the
1980 to 1999 average value. The
observed timeseries is shown by
the thick black line. The panels
shown are for the a CCSM3, b
CSIRO-Mk3.0, c GFDLCM2.1, d GISS-ER, e
MIROC3.2(hires), and f
UKMO-HadCM3
Bellingshausen Sea and extending into the Atlantic. The
compensating anomalies in different regions are partly
responsible for the relatively small trends observed in the
total Antarctic ice extent as discussed above. Other relatively small anomalies are present around the rest of the
continent and there is the hint of a wave-3 type structure. The pattern exhibited by EOF1 has been termed
the ‘‘ADP’’ (Yuan and Martinson 2000, 2001) and is
seen in other southern ocean fields including sea surface
temperature, surface air temperature, and SLP.
The second EOF from the observed sea ice concentration accounts for approximately 15% of the variance.
It exhibits a pattern which is quite similar to EOF1 but
rotated eastward. EOF analysis identifies propagating
anomalies as a pair of EOFs with similar variance that
are in quadrature (e.g., Bretherton 2003). Thus, a lagged
correlation analysis can reveal a propagating structure.
While the variance of the first two EOFs are quite different they are significantly correlated at a one year lag
with R=0.55, which may indicate the propagation of
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
235
Fig. 6 The 20-year running standard deviation of the total annual
average southern hemisphere ice extent from the model simulations
over the twentieth century. The values are shown as the percent
increase from the observed value
anomalies. This would be consistent with the advection
of anomalies by the Antarctic circumpolar current
(ACC) and with Antarctic circumpolar wave (ACW)
(White and Peterson 1996) type behavior.
The leading EOFs obtained from the simulated winter sea ice concentration from 1960 to 1999 for the different models are shown in Fig. 8. We have also
performed an EOF analysis for the entire 20th century
timeseries and for the 1979 to 2000 timeseries of winter
ice concentration. The results differ somewhat for the
different time periods in terms of the details of the spatial patterns and the percent variance that is explained
by the different EOFs. However, qualitatively, the results are quite similar to those using the timeseries from
1960 to 1999. Thus, we show and discuss results from
1960 to 1999 for consistency with Raphael and Holland
(Part I, this issue). The simulated ice concentration has
been linearly detrended in order to remove any trends
associated with model spin-up issues. This only makes a
considerable difference for the CSIRO-Mk3.0 analysis.
While we compare these to the EOFs computed from the
observed raw timeseries data, nearly identical results are
obtained from an analysis of detrended observations.
Both the CCSM3 and the MIROC3.2 (hires) exhibit
dipole-like variability as the leading mode of winter sea
ice concentration similar to the observations. However
there are differences in the extent of the Atlantic
anomalies and the representation of the smaller anomalies in the Western Pacific and Indian Ocean sectors as
compared to the observations. In particular, the CCSM3
simulation obtains more extensive Atlantic anomalies
and the MIROC3.2(hires) simulation obtains less
extensive Atlantic anomalies as compared to the observations. However, some of these details are dependent
on the exact time period used for analysis, suggesting
Fig. 7 The first two empirical orthogonal functions from the
observed winter sea ice concentration from 1979 to 1999. In this,
and all future plots, the nondimensional EOFs have been scaled by
the standard deviation of the corresponding principal component
timeseries to show the dimensional standard deviation at each grid
point associated with the EOF. The contour interval is 5%, the zero
contour is omitted, and negative values are shaded
that there are small variations that occur in this spatial
structure and that these differences from the observations may not be robust. In addition to simulating a
reasonable dipole-like structure as the leading mode of
variability, the magnitude of the anomalies associated
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Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
Fig. 8 The first EOF of winter
sea ice concentration from the
model simulations from 1960 to
1999 using linearly detrended
data. Shown are a CCSM3, b
CSIRO-Mk3.0, c GFDLCM2.1, d GISS-ER, e
MIROC3.2(hires), and f
UKMO-HadCM3. The contour
interval is 5%, the zero contour
is omitted, and negative values
are shaded
with EOF1 in the CCSM3 and MIROC3.2(hires) models
compare quite well to the observed values as does the
variance explained by this leading mode. The results
found here for the CCSM3 model are similar to the results in Holland et al. (2005) from a long control run of
an earlier version of this model, the CCSM2.
The GISS-ER and CSIRO-Mk3.0 simulations both
exhibit anomalies of opposite sign in the Pacific and
Atlantic sectors associated with the leading mode of ice
concentration variability. However, the location of the
centers of the anomalies are considerably different than
those in the observations, making it questionable that
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
237
Fig. 10 The correlation of the leading mode of ice variability and
the winter ice extent as a function of longitude and lag from
observations. Negative lags indicate that the longitudinal ice extent
is leading the EOF1 timeseries. The contour interval is 0.2 and the
zero contour is omitted. Negative values are shaded. The Antarctic
continental outline is shown at the bottom of the plot for reference
Fig. 9 The first EOF from high-pass filtered winter sea ice
concentration from a CSIRO-Mk3.0, b GFDL-CM2.1 and c
GISS-ER. The contour interval is 5%, the zero contour is omitted,
and negative values are shaded
these can be considered the same mode of variability as
seen in the observations. The CSIRO-Mk3.0 anomalies
are rotated eastward from their observed locations
whereas the GISS-ER Pacific sector anomaly is located
westward from the observed location. Additionally, the
magnitude of the anomalies is quite small compared to
the observations and the variance explained by these
leading modes is considerably smaller than that of the
observations. The leading EOFs from these models vary
primarily at low-frequencies, with the principal component timeseries of the GISS-ER EOF1 having significant
power at interdecadal timescales and that of the CSIROMk3.0 EOF1 having dominant power at very low frequencies (not shown).
While the UKMO-HadCM3 model does exhibit
opposing anomalies between the Ross Sea region and
the Bellingshausen Sea (as in the observations), the
largest anomalies are located in the Western Pacific and
Indian Sectors. This is a region of quite modest variability in the observed ice cover. The GFDL-CM2.1 also
exhibits anomalies of opposite sign between the Ross
Sea and Bellingshausen Sea extending into the Atlantic.
However, in contrast to the observations, the anomalies
at Drake Passage do not extend throughout the western
Atlantic. Instead, anomalies of opposite sign are present
between Drake Passage and the eastern Weddell Sea.
The Weddell Sea timeseries of ice extent varies predominantly at low-frequency timescales in the GFDLCM2.1.
While the CCSM3 and MIROC3.2(hires) models
seem to have reasonable dipole-like variability, the
remaining simulations do not generally obtain a leading
mode of winter sea ice concentration variability that
exhibits the dipole-like structure seen in the observations. The observational record is quite short however
and the observed timeseries will not capture interdecadal
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Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
Fig. 11 As in Fig. 9 for the different model simulations using the high-pass filtered EOFs. Shown are the analysis from a CCSM3,
b CSIRO-Mk3.0, c GFDL-CM2.1, d GISS-ER, e MIROC3.2(hires), and f UKMO-HadCM3
changes in the sea ice. As mentioned above, several of
the models obtain a leading EOF that is dominated by
low-frequency variability. As we are analyzing the model
results over a longer period of time (1960–1999) than the
observed record (1979–1999), it is possible that they are
picking up a real mode of variability that is unrealizable
in the short observed timeseries or that the models obtain unrealistic low-frequency oscillations. In either case,
this might contaminate an ADP-like signal in these
models. To address this possibility, an EOF analysis was
performed on high-pass filtered winter ice concentration
from the models in which only timescales with periods of
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
Fig. 12 Correlation (a) and regression (b) of NCEP SLP (averaged
from April–June) on the leading EOF of winter sea ice concentration from the observations for 1979–1999. In (a) points that are
stippled have significant correlations and the contour interval is
0.1. The contour interval in (b) is 1 mb per standard deviation of
the principal component timeseries. Negative values are shaded
239
models, more dipole-like anomalies are now associated
with the leading EOF, although the exact structure and
extent of the anomalies have some discrepancies when
compared to observations. In particular, the GFDLCM2.1 simulation obtains anomalies in the Bellingshausen Sea but these have limited extension into the
Atlantic sector. This is consistent with biases in the
mean ice edge of the GFDL-CM2.1 model which has
too little ice cover in the Weddell Sea region. The
magnitude of the anomalies associated with EOF1 and
the percent variance explained by this EOF (24%)
compare quite well to the observations. In contrast,
while the placement of the anomalies in the CSIROMk3.0 model may arguably compare more favorably to
the observations, the magnitude of the anomalies in
this simulation associated with EOF1 are weak and this
leading mode of variability accounts for less than 20%
of the variance. In the GISS-ER simulation, the leading
EOF from the high-pass filtered data exhibits fluctuations in the Atlantic sector ice cover that are very
similar in structure to the leading mode of variability
present in the raw data but anomalies in the Pacific/
Indian sectors are essentially absent and the high-pass
filtered analysis does not exhibit dipole-like anomalies.
In summary, the analysis shown here suggests that
the CCSM3 and MIROC3.2(hires) models obtain reasonable dipole-like sea ice variability. Additionally, the
GFDL-CM2.1 and CSIRO-Mk3.0 have dipole-like
anomalies when high-pass filtered data (with periods less
than 20 years) are used for the analysis. However, in the
case of the CSIRO-Mk3.0 model the anomalies associated with this leading EOF are quite weak. The leading
mode of variability in the UKMO-HadCM3 simulations
has reasonable dipole-like variations in the Pacific/
Atlantic regions, but variability in the Indian ocean
sector of the southern ocean is considerably larger than
the observations. It is questionable whether the GISSER obtains a dipole-like structure of sea ice anomalies.
There are some compensating anomalies between the
Atlantic and Pacific sectors when the raw data are used
for the EOF analysis. However, an analysis of the highpass filtered data is dominated by variability in the
Atlantic sector, with no compensating anomalies in the
Pacific.
4.3 Eastward propagation of sea ice anomalies
less than 20 years were retained. This analysis is not
particularly sensitive to the exact frequency cutoff and
similar results are obtained using a filter which retains
only timescales with periods less than 10 years.
For the CCSM3, MIROC3.2(hires), and UKMOHadCM3 models, the leading EOF from the high-pass
filtered analysis is quite similar to that in Fig. 8 and is
not shown. However, as shown in Fig. 9, the leading
mode of variability is considerably different in the
GFDL-CM2.1 and CSIRO-Mk3.0 runs and somewhat
different in the GISS-ER run when high-pass filtered
data is used. For the GFDL-CM2.1 and CSIRO-Mk3.0
Observations of the southern hemisphere sea ice exhibit
an eastward propagation of anomalies. This has been
documented as part of the ACW phenomenon (White
and Peterson 1996) and is suggested by the significant
correlation between the first and second EOFs of winter
sea ice at a one-year lag. Many of the models exhibit a
similar relationship between the leading modes (EOF1
and EOF2) of sea ice variability, suggesting a similar
propagation of the ice anomalies.
To more clearly quantify the propagation of anomalies associated with the leading mode of ice variability,
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Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
Fig. 13 The regression of SLP (averaged from April–June) on the
leading EOF of high-pass filtered winter sea ice concentration. a
CCSM3, b CSIRO-Mk3.0, c GFDL-CM2.1, d GISS-ER, e
MIROC3.2(hires), and f UKMO-HadCM3. Stippled points indicate
significant values at the 95% level, the contour interval is 1 mb per
standard deviation of the principal component timeseries and
negative values are shaded
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
we have correlated the ice extent anomalies with the
leading mode of ice variability as a function of longitude
and lag. The observational analysis is shown in Fig. 10
and indicates that the anomalies associated with the
leading mode of sea ice variability do propagate eastward with the Antarctic circumpolar current. As discussed by Gloersen and White (2001), the anomalies
survive from one winter to the next because of the
influence of the anomalous sea ice conditions on the
solar radiation absorbed in the ocean, the resulting sea
surface temperature anomalies, and the ice growth rates
the following fall. While eastward propagation of the
anomalies is present in the observations, it is strongest in
the Pacific sector and the anomalies appear to generally
dissipate within the eastern Atlantic/Indian sectors of
the southern ocean. This is consistent with observational
studies which show that while southern hemisphere sea
ice anomalies may have completely encircled the Antarctic continent during some time periods (consistent
with ACW behavior), they have had only limited eastward propagation during other time periods (Connolley
2003).
Analysis of the model integrations shows considerable differences in the eastward propagation of the
anomalies associated with the leading mode of ice
variability (Fig. 11). The CSIRO-Mk3.0 simulation
shows essentially no eastward propagation of the
anomalies, whereas the GFDL-CM2.1, the MIROC3.2(hires), and the UKMO-HadCM3 show only
weak propagation which occurs in the Pacific sector.
The CCSM3 simulation has quite strong eastward
propagation which is strongest within the Pacific sector
of the southern ocean. The CCSM3 does show some
propagation of anomalies from the Pacific to the
Atlantic. In the GFDL-CM2.1 run Drake passage appears to act as an obstacle that prevents the further
eastward propagation of the anomalies. The GISS-ER
simulation, which arguably has a leading mode of ice
variability that least resembles the observed variability,
has quite weak propagation of ice anomalies. However,
our analysis suggest that these anomalies can encircle
the Antarctic continent.
Overall, it appears that the models do not consistently simulate an eastward propagation of ice anomalies. The reasons for this are probably quite variable
and complex. As discussed by Holland et al. (2005) in
an analysis of a single long control model integration,
the speed and direction of the Antarctic circumpolar
current in the region of the ice anomalies is an
important consideration for whether a model simulates
eastward propagation of the ice anomalies. Thus, biases in the ocean currents or the location of the ice
anomalies can influence whether the models simulate
this eastward propagation. While it is beyond the scope
of the present study, it is likely that for the models
considered here both of these things contribute in differing degrees to the wide range in simulated conditions.
241
5 Forcing of the leading mode of ice variability
5.1 Associated SLP anomalies
Observational and model analysis (e.g., Yuan and
Martinson 2000; Liu et al. 2004; Holland et al. 2005)
suggests that the southern hemisphere sea ice variability
is influenced by SLP (and associated wind and atmospheric/oceanic heat advection) anomalies. The location
and magnitude of these SLP variations influences the
resulting sea ice anomalies. To investigate the mechanisms that are driving the simulated ice variability and
how this compares to the observed conditions, we
examine the SLP variability in the models and how it
relates to the leading sea ice EOF. We use the EOFs
obtained from high-pass filtered data for this analysis.
An analysis of SLP correlated with the sea ice variability shows that the largest correlations are obtained
when the SLP leads the winter ice variations by one
season. Figure 12 shows the NCEP/ NCAR reanalysis
April–June averaged SLP (Kalnay et al. 1996) correlated
and regressed on the leading mode of observed wintertime sea ice variability. The sea ice variability is associated with anomalously low SLP over much of the
southern ocean and Antarctica. These SLP anomalies
are not zonally symmetric, but instead have a maximum
in the Amundsen/Bellingshausen Sea region, with a decrease of approximately 4 mb associated with the leading mode of ice variability. As shown by Raphael and
Holland (this issue), the SAM variations influence the
SLP in this region (and in fact often look quite similar to
the patterns shown in Fig. 12) and should contribute to
anomalies there. This is discussed further below. The
anomalous winds associated with these SLP variations
will dynamically force the sea ice conditions by driving
anomalous equatorward ice transport in the Ross and
Amundsen Seas and by driving anomalous poleward ice
transport in the Bellingshausen and Weddell Seas.
Thermal forcing of the sea ice, associated with anomalous atmospheric and (wind-driven) oceanic heat
advection should also contribute to the sea ice anomalies. Holland et al. (2005) looked at the contributions
from these different effects in a single long control run of
a fully coupled model and found that the Pacific
anomalies were largely driven by changes in ice transport and ocean circulation that modified the oceanic
heat flux convergence. In the Atlantic, no single process
dominated in forcing the anomalous sea ice conditions,
but instead there were contributions from changing
ocean and sea ice circulation and surface heat fluxes.
An analysis of the SLP regressed on the leading mode
of sea ice variability from the model simulations is
shown in Fig. 13. In the models which obtain ADP-like
sea ice variations as the leading mode of variability in
the high-pass filtered data, significant anomalously low
SLP is present in the Amundsen/Bellingshausen Sea region. This includes the CCSM3, GFDL-CM2.1,
242
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
CSIRO-Mk3.0, MIROC3.2(hires), and UKMO-HadCM3 simulations. This suggests that, as in the observations, SLP variability in this region forces the sea ice
conditions in the Pacific and Atlantic sectors. In the
UKMO-HadCM3 run, the strong Indian sector sea ice
variability is related to a strong anomalous SLP gradient
which is present in the Western Pacific/Indian sector
regions (Fig. 13f). While the observations show some
anomalous SLP associated with the sea ice variability
there (Fig. 12), it is considerably weaker than in the
UKMO-HadCM3 simulation.
All of the model simulations and the observations
obtain a maximum high southern latitude SLP variance
in the Pacific sector of the southern ocean (not shown).
As these appear to drive the observed ADP like sea ice
anomalies, it is perhaps not too surprising that the
leading mode of simulated ice variability exhibits this
structure. However, as discussed by Holland et al.
(2005) from modeling results it appears that the forcing
of the sea ice conditions is complex and that changes in
ocean circulation, sea ice transport, and thermal fluxes
are all important considerations which differ in different
regions. As such, there are numerous processes/biases
that may affect the sea ice response to the SLP anomalies, including biases in the mean state, subtleties in the
orientation of the SLP anomalies, biases in the ocean
circulation and sea ice drift response to the SLP anomalies, and biases in the wind driven atmospheric heat
transport anomalies.This suggests that the model simulations are accurately representing various complex
processes to obtain the correct response in the ice cover.
However, further work is required to determine more
fully the forcing mechanisms driving the sea ice conditions in the various models.
For the GISS-ER model, the ADP-like fluctuations
are generally not well represented as the leading mode of
ice variability. The location of the maximum SLP variance in this run may in part be responsible. This maximum variability is in the Ross Sea region (instead of the
Amundsen/Bellingshausen Sea as observed) which may
affect the ice variability.
5.2 Relationship to atmospheric/coupled modes
of variability
An analysis of important modes of Antarctic atmospheric variability in these model simulations is discussed in Raphael and Holland (this issue). From
previous studies and the analysis shown above, it appears that atmospheric variability is important for
forcing the southern hemisphere sea ice conditions. Here
we diagnose the relationship between the simulated
SAM of atmospheric variability discussed in Raphael
and Holland and the leading mode of sea ice variability
discussed above. As discussed by Raphael and Holland,
all of the model runs do clearly simulate a SAM, although the spatial pattern and timeseries of the SAM
variability differ among the different models.
Previous observational analysis (Liu et al. 2004) and
modeling studies (Hall and Visbeck 2002; Holland et al.
2005) support that this mode of atmospheric variability
is important for southern hemisphere sea ice conditions.
Also, as observations suggest that the ENSO influences
southern hemisphere sea ice conditions (e.g., Carleton
1988; Simmonds and Jacka 1995; Ledley and Huang
1997; Kwok and Comiso 2002), we assess relationships
with the simulated ENSO as well. A number of other
papers (e.g., Capotondi et al. 2005) have more extensive
information on the ENSO simulations in some of the
models discussed here.
The correlation of the SAM index from observations
and the various model simulations with the leading
mode of sea ice variability is shown in Table 1. The
correlations are shown for an April/ May/June (AMJ)
averaged SAM index since the analysis in Sect. 5.1 shows
that the sea ice is influenced by SLP anomalies during
this time. The observations show that the fall SAM is
significantly correlated to sea ice conditions in the following season, suggesting that the sea ice variability is
being forced in part by the SAM. However, the correlation is generally quite weak indicating that the SAM is
responsible for only a modest amount of the variance
associated with the leading mode of sea ice variability.
Almost all of the model simulations show a significant correlation between the AMJ SAM index and the
leading mode of sea ice variability from high-pass filtered data. The exception is the GISS-ER model, which
does show a positive correlation, but the correlation is
not significant at the 95% level. For most of the models,
the correlation is quite small and is similar to the
observations. This suggests that variations in the SAM
modify the sea ice conditions, but only account for a
relatively small amount of the variance in ice conditions
associated with the leading mode of sea ice variability.
However, in the UKMO-HadCM3 simulation, the SAM
index is correlated to the sea ice EOF1 at greater than
R=0.8, suggesting that in the UKMO-HadCM3 simulation, variations in the SAM are driving a considerable
fraction of the variance in the leading mode of ice variability. In fact, as shown by Raphael and Holland (this
issue), the SAM spatial structure in the UKMO-HadCM3 run looks very similar to the SLP regressed on the
Table 1 Correlations of the leading mode of sea ice variability and
the southern annular mode (SAM) for the observations and model
simulations
Observations
CCSM3
CSIRO-Mk3.0
GFDL-CM2.1
GISS-ER
UKMO-HadCM3
AMJ SAM and
high-pass filtered
EOFs
AMJ SAM and
detrended EOFs
0.47
0.40
0.54
0.39
0.30
0.84
0.47
0.44
0.14
0.19
0.20
0.80
Bold values are significant at the 95% level accounting for the
autocorrelation of the timeseries
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
leading mode of ice variability (Fig. 13e). The importance of the SAM for the ice variability in the UKMOHadCM3 run may be partly responsible for the enhanced variability in the Indian ocean sector in this
simulation as compared to observations.
The correlation of the Nino3.4 index (used here as a
measure of the strength of ENSO) and the leading mode
of sea ice variability from the high-pass filtered data is
shown in Fig. 14. The observations indicate that ENSO
leads changes in the sea ice with significant, although
weak, correlations during the year prior to the sea ice
variability. Similar to the SAM index, this suggests that
ENSO is in part forcing changes in the sea ice associated
with the leading mode of ice variability but only accounts for a relatively small amount of the variance in
those ice conditions. Many of the models have a similar
relationship, although the correlations vary. The
CCSM3 and MIROC3.2(hires) both have significant
correlations leading the ice conditions, but these correlations are only significant when ENSO precedes the ice
conditions by several months (compared to the
12 months seen in the observations). The CSIROMk3.0, GFDL-CM2.1, and UKMO-HadCM3 simulations compare quite well to the observations, in terms of
both the value and lagged-relationship of the correlations. The GISS-ER shows little significant correlation
to the sea ice conditions.
As discussed by Capotondi et al. (2005), the ENSO
simulations in these and other models participating in
the IPCC-AR4 vary considerably both in terms of their
magnitude and timescales. It is likely that these details of
the ENSO simulations in the models will influence the
teleconnections to the southern hemisphere and hence
the related sea ice conditions. Figure 15 shows a scatter
243
Fig. 15 A scatter plot of the Nino3.4 standard deviation and the
minimum correlation from Figure 14 when Nino3.4 leads the ice
variability
plot of the standard deviation of the Nino3.4 index in
the models versus the minimum correlation found between the simulated Nino3.4 index and the leading mode
of ice variability when Nino3.4 leads the ice conditions
by 1–12 months. Interestingly, models with larger variance in the Nino3.4 sea surface temperatures have
stronger correlations with the southern hemisphere sea
ice conditions, suggesting that the ENSO teleconnections to the southern hemisphere are indeed dependent
on the magnitude of the ENSO events. It is likely that
the timescales of ENSO will also affect the southern
hemisphere sea ice conditions, as the sea ice has considerable ‘‘memory’’ and anomalies can build over a
number of years. For example, the CCSM3 simulation
has a very dominant 2-year period in the ENSO variability (e.g., Capotondi et al. 2005) meaning that an ElNino event is quickly followed by a La Nina event. This
could explain why the correlations in the CCSM3 are
somewhat weaker than in the observations and much
shorter-lived, even though the magnitude of the ENSO
variability is quite reasonable.
6 Conclusions and discussion
Fig. 14 The correlation of the Nino3.4 index and the leading mode
of sea ice variability. Significant correlations (at the 95% level) are
denoted by an asterisk
We have assessed the ability of a number of twentieth
century fully-coupled model simulations to represent
southern hemisphere mean and variable sea ice conditions. The model runs considered for this study are
participating in the Intergovernmental Panel on Climate Change fourth assessment report (IPCC-AR4)
and include the CCSM3, CSIRO-Mk3.0, GFDLCM2.1, GISS-ER, MIROC3.2(hires), and UKMOHadCM3. These models differ in their spatial resolution and representation of physical processes, but they
all contain active atmosphere, ocean, land and sea ice
components and are driven by temporally evolving
external forcing, including greenhouse gas changes,
244
Holland and Raphael: Twentieth century simulation of the southern hemisphere climate in coupled models
tropospheric and stratospheric ozone changes, solar
input variations and volcanic forcing from the twentieth century.
The simulations obtain a reasonable annual cycle of
ice extent with a largely seasonal ice pack. There is no
consistent bias in the simulated monthly climatological
ice extent when compared to the observations, but there
is a considerable scatter among the different models.
From a correlation of the gradient of zonally averaged
SLP at 60S and the maximum winter ice extent simulated by the different models, it appears that much of
this scatter is associated with wind forced sea ice transport.
Compared to the short observational record, the
models obtain too much variance in the annual averaged
southern hemisphere ice extent. No individual region
appears responsible for the excessive variance in the ice
extent. Instead, it is possible that compensating anomalies that are present in the interannual variability of the
observed ice cover are absent (or their compensation is
reduced) in the model simulations.
Many of the models obtain a leading mode of wintertime sea ice concentration variability that resembles
the ‘‘ADP’’ spatial pattern of the observations which has
anomalies of one sign over much of the Pacific and
anomalies of opposite sign in the Bellingshausen Sea and
extending into the Atlantic. Two of the models (the
CCSM3 and MIROC3.2(hires)) obtain this pattern and
a reasonable magnitude of the associated ice anomalies
from the raw model data. A number of other simulations
(CSIRO-Mk3.0 and GFDL-CM2.1) obtain ADP-like
variability when the sea ice concentrations are high-pass
filtered to exclude timescales with periods longer than
20 years. However, in the case of the CSIRO-Mk3.0, the
associated ice anomalies are quite weak. The UKMOHadCM3 has ADP-like features in the leading mode of
ice variability, but fluctuations in the Indian sector of the
southern ocean are considerably larger than those in the
observations.
While the observations show an eastward propagation of sea ice anomalies consistent with the ACW
phenomenon (White and Peterson 1996), the models do
not typically exhibit this behavior. The CCSM3 obtains
reasonable eastward propagation within the Pacific
sector and shows some limited propagation from the
Pacific to the Atlantic. This is quite similar to results
obtained by Holland et al. (2005) from a long control
integration of an earlier version of the CCSM model.
The GFDL-CM2.1, MIROC3.2(hires), and UKMOHadCM3 appear to have weak propagation which is
isolated to the Pacific sector. The GISS-ER simulation,
which has the most questionable ADP-like behavior,
obtains ice anomalies which appear to encircle the
Antarctic continent. However, the correlations are
quite weak. It is possible that some of the discrepancies
between the models and observations could be associated with the relatively short record of satellite sea ice
observations which are used in our analysis. For
example, Connolley (2003) has shown in an analysis of
conditions from 1968 to 1999 that the ACW is only
clearly visible from 1985 to 1994. It is also possible that
model deficiencies associated with the Antarctic circumpolar current speed, the efficiency of atmosphereocean coupling, or the location of sea ice anomalies
contribute to the lack of eastward propagation of the
ice anomalies (e.g., White et al. 1998; Christoph et al.
1998).
Consistent with observations, the model simulations
show that SLP anomalies in the Amundsen/Bellingshausen Sea region typically precede the winter sea ice
conditions by a season. This suggests that the ice
anomalies are partially wind driven, although changes in
thermal forcing due to anomalous oceanic and atmospheric heat flux convergence are also potentially
important. Both the SAM and the ENSO appear to
influence the southern hemisphere ice conditions in the
model integrations, although to different degrees in
different models. The ENSO-Southern hemisphere teleconnections appear to be influenced by the amplitude of
simulated ENSO events, with stronger teleconnections
(as indicated by the sea ice analysis) present in simulations which have a higher standard deviation of the
Nino3.4 sea surface temperature timeseries.
Acknowledgements This research was funded by NSF and DOE as
a Climate Model Evaluation Project (CMEP) grant,# NSF ATM
0444682, under the U.S. CLIVAR Program (http://www.usclivar.org/index.html). We acknowledge the international modeling
groups for providing their data for analysis, the Program for
Climate Model Diagnosis and Intercomparison (PCMDI) for
collecting and archiving the model data, the JSC/CLIVAR
Working Group on Coupled Modelling (WGCM) and their
Coupled Model Intercomparison Project (CMIP) and Climate
Simulation Panel for organizing the model data analysis activity,
and the IPCC WG1 TSU for technical support. The IPCC Data
Archive at Lawrence Livermore National Laboratory is supported
by the Office of Science, U.S. Department of Energy. This research uses data provided by the Community Climate System
Model project (http://www.ccsm.ucar.edu), supported by the
Directorate for Geosciences of the National Science Foundation
and the Office of Biological and Environmental Research of the
U.S. Department of Energy. We would like to thank Dr. Peter
Gent for comments on a draft of this manuscript. We also extend
our thanks to Dr. W.M. Connolley and an anonymous reviewer
who provided very useful comments that led to considerable
improvements in this manuscript.
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