15 SEPTEMBER 2007

DESER ET AL.

4751

The Transient Atmospheric Circulation Response to North Atlantic SST and
Sea Ice Anomalies
CLARA DESER AND ROBERT A. TOMASNational Center for Atmospheric Research, Boulder, Colorado

SHILING PENGNOAA/Earth System Research Laboratory, Boulder, Colorado(Manuscript received 28 July 2006, in final form 12 February 2007)
ABSTRACTThe objective of this study is to investigate the transient evolution of the wintertime atmospheric circulation response to imposed patterns of SST and sea ice extent anomalies in the North Atlantic sector usinga large ensemble of experiments with the NCAR Community Climate Model version 3 (CCM3). The initialadjustment of the atmospheric circulation is characterized by an out-of-phase relationship between geopotential height anomalies in the lower and upper troposphere localized to the vicinity of the forcing. Thisinitial baroclinic response reaches a maximum amplitude in ⬃5–10 days, and persists for 2–3 weeks. Diagnostic results with a linear primitive equation model indicate that this initial response is forced by diabatic
heating anomalies in the lower troposphere associated with surface heat flux anomalies generated by theimposed thermal forcing. Following the initial baroclinic stage of adjustment, the response becomes progressively more barotropic and increases in both spatial extent and magnitude. The equilibrium stage ofadjustment is reached in 2–2.5 months, and is characterized by an equivalent barotropic structure that
resembles the hemispheric North Atlantic Oscillation–Northern Annular Mode (NAO–NAM) pattern, themodel’s leading internal mode of circulation variability over the Northern Hemisphere. The maximum
amplitude of the equilibrium response is approximately 2–3 times larger than that of the initial response.
The equilibrium response is primarily maintained by nonlinear transient eddy fluxes of vorticity (and, to alesser extent, heat), with diabatic heating making a limited contribution in the vicinity of the forcing.

1. Introduction
Considerable progress has been made over the past
decade in understanding the mechanisms generating
the equilibrium atmospheric circulation response to extratropical SST anomalies (Robinson 2000; Kushnir etal. 2002; Peng et al. 2003). It is now clear that the equilibrium response depends upon the nature and strengthof transient eddy feedbacks, particularly eddy vorticity
fluxes, resulting from interactions between the heatingforced anomalous flow and the storm tracks (Kushnir
and Lau 1992; Ting and Peng 1995; Peng and Whitaker
1999; Peng et al. 2003). These transient eddy feedbacks
may substantially modify the direct baroclinic response

Corresponding author address: Dr. Clara Deser, Climate andGlobal Dynamics Division, NCAR, P.O. Box 3000, Boulder, CO
80307-3000.
E-mail: cdeser@ucar.edu
DOI: 10.1175/JCLI4278.1© 2007 American Meteorological Society

JCLI4278

to anomalous diabatic heating (Hoskins and Karoly
1981; Hendon and Hartmann 1982) into an equivalentbarotropic response that extends well beyond the region of forcing (Peng et al. 2003; Magnusdottir et al.
2004). Thus, different atmospheric models may produce
different responses to the same SST anomaly depending on their simulation of the location and intensity of
the climatological mean storm tracks (Peng and Whitaker 1999; Walter et al. 2001; Hall et al. 2001). Anotherimportant and related factor influencing the atmospheric response to extratropical SST anomalies is the
structure of the model’s intrinsic variability, which is
known to be mainly eddy driven (Branstator 1992; Ting
and Lau 1993). Depending on the location of the SST
forcing, the response may project onto the dominantmodes of internal atmospheric variability thereby
changing their frequency of occurrence (Peng and Robinson 2001; Hall et al. 2001; Peng et al. 2003; Magnusdottir et al. 2004; Deser et al. 2004; Cassou et al. 2007).

4752

JOURNAL OF CLIMATEThus, models with different structures of intrinsic variability may produce different hemispheric-wide responses to the same SST anomaly.
All of the studies cited above are based upon diagnosis of the equilibrium atmospheric response to extratropical SST anomalies. An alternative approach is to
examine the transient adjustment of the atmosphericcirculation to imposed thermal forcing. Two recent
modeling studies, Li and Conil (2003) and Ferreira andFrankignoul (2005), have investigated aspects of thisadjustment process to North Atlantic SST anomalies.
Li and Conil (2003) examined the initial (days 1–8)transient evolution of the atmospheric response over
the North Atlantic and Europe to an observed tripole
pattern of SST anomalies in the North Atlantic basedon a 240-member ensemble of perpetual January integrations of the Laboratoire de Météorologie DynamiqueZoom (LMDZ) general circulation model (GCM), a
19-level model with approximately 60-km horizontal
resolution over the North Atlantic (Li 1999). They
found that initially (days 1–2) the response is baroclinic
and localized to the vicinity of the forcing, while overthe next 6 days the response becomes more barotropic
and exhibits some downstream development. However,the limited duration of their experiments (8 days) did
not allow them to assess how long it takes for the response to come to equilibrium, and their focus on the
North Atlantic–European sector did not afford an examination of the full spatial scale of the response.
Ferreira and Frankignoul (2005) examined the atmospheric response to a North Atlantic SST tripole
anomaly pattern similar to that in Li and Conil (2003)
using a three-level quasigeostrophic model solved at
T31 resolution (approximately 3.75° latitude and 3.75°
longitude) under perpetual wintertime conditions.
They conducted a 400-member ensemble of integrations each 10 months in duration, long enough to reachequilibrium. They found that the initial atmospheric
response is forced by diabatic heating anomalies associated with anomalous air–sea heat fluxes and its structure is similar to that predicted by linear theory. Thisinitial circulation response rapidly modifies the transient eddy activity, which in turn transforms the initial
baroclinic response into an equivalent barotropic one.
This equivalent barotropic response resembles theanomalous atmospheric circulation pattern that createdthe SST tripole anomaly in the first place (determinedfrom experiments with an interactive ocean mixed layer
model), thus acting as a positive feedback. The adjustment time for the establishment of the barotropic response was 3–4 months for the circulation pattern resembling the North Atlantic Oscillation–Northern Annular Mode (NAO–NAM; Hurrell 1995; Thompson

VOLUME 20

and Wallace 1998), the leading structure of internal
wintertime atmospheric variability over the Northern
Hemisphere. It remains to be seen whether the adjustment time in a three-level quasigeostrophic model isindicative of that in a more complex atmospheric GCM.
Deser et al. (2004, hereafter D04) and the companion
study of Magnusdottir et al. (2004, hereafter M04) used
an atmospheric GCM to examine the equilibrium circulation response to two types of boundary forcing: sea
ice and SST. These boundary forcings were specified tohave realistic spatial patterns based on observed trends
over the North Atlantic sector during the second half ofthe twentieth century. Varying the magnitude and signof the forcing patterns, D04 and M04 found that the
responses scaled linearly with amplitude but nonlinearly with polarity. For the cases of SST forcing withreversed polarity (consisting mainly of a positive
anomaly in the subpolar gyre) and sea ice forcing withobserved polarity (reduced ice extent in the Greenlandand Barents Seas and increased ice extent in the Labrador Sea), the wintertime atmospheric circulation responses were found to be very similar, resembling thenegative phase of the NAO–NAM, the leading mode ofvariability in a long control run of the model.
D04 partitioned these responses into a component
that projects onto the leading mode of internal variability and a residual from that projection. They found thatthese two components of the response exhibit distinctive vertical and spatial structures, suggestive of different underlying dynamical mechanisms. In particular,
the residual component exhibits a baroclinic verticalstructure confined to the vicinity of the forcing, while
the internal projection component exhibits an equivalent barotropic vertical structure that is hemispheric inscale. This empirical separation of the response into
two distinct components led them to speculate that theresidual component is maintained by diabatic heating
anomalies associated with the surface boundary forcing
and that the internal projection component is maintained primarily by transient eddy flux convergences ofheat and vorticity, as suggested in earlier mechanistic
studies (e.g., Ting and Peng 1995; Peng and Whitaker
1999; Peng and Robinson 2001).
The objective of this study is to further our understanding of the nature of the transient evolution of theatmospheric circulation response to mid- and high-latitude SST and sea ice forcing using experiments with the
National Center for Atmospheric Research (NCAR)
Community Climate Model version 3 (CCM3). The
SST and sea ice forcing patterns are identical to those in
D04 and M04 described above. To enhance the robustness of the results, we conduct a large (240 member)
ensemble of simulations for each type of boundary forc-

15 SEPTEMBER 2007

DESER ET AL.

4753

FIG. 1. (top) Monthly SST anomalies used to force CCM3, the SST experiment. Solid contours and dark shading denote positive
anomalies; dashed contours and light shading denote negative anomalies. The contour interval is 2 K and only odd numbered contours
are shown (e.g., ⫹/⫺ 1 K, 3 K, . . . ). (Bottom) Monthly sea ice extent anomalies used to force CCM3, the ICE experiment. Areas
enclosed by dark (light) shading denote regions where sea ice has been removed from (added to) the climatological distribution.

ing, as well as comparing the ensemble mean responses
to sea ice versus SST forcing. We employ a linear atmospheric primitive equation model to diagnose themaintenance of the transient response at different
stages of its evolution. Our investigation is guided by
the following questions. What are the structures of theinitial and equilibrium responses, and what are the processes that maintain them? What is the adjustment time
for the establishment of the equilibrium response? Is
there a physical basis for the empirical decompositionof the equilibrium response performed in D04?
The paper is structured as follows: section 2 describes
the experiments with CCM3 and the linear primitive
equation model; section 3 presents the results; and Section 4 provides a discussion and conclusions.

2. Model experiments
a. CCM3
The spatial patterns of the North Atlantic SST and
sea ice anomalies used to force CCM3 are identical tothose in D04 and are based upon observed trends during the second half of the twentieth century. For thepresent study, we have selected the amplitude and polarity of the forcing patterns that D04 found gave thestrongest and most similar responses (e.g., the negative
phase of the NAO–NAM). Specifically, the SST forcing
is defined as the observed SST trend computed separately for each month from the Global Sea Ice and Sea
Surface Temperature version 2 dataset (GISST2)
dataset (Raynor et al. 1996) during 1954–94 over the

North Atlantic north of 30°N, multiplied by a factor of⫺5 (i.e., the polarity of the observed trend is reversed
and its amplitude magnified fivefold). Similarly, the sea
ice forcing is defined as the observed monthly trend in
sea ice extent over the North Atlantic–Arctic region
during 1958–97 from Deser et al. (2000), multiplied byapproximately a factor of 2. Note that a grid cell iseither ice free or 100% ice covered; there is no fractional ice concentration. Since sea ice is not explicitly
represented in the model, only surface properties suchas albedo and the surface energy fluxes change when
the sea surface is covered by ice. Both the monthly SST
and sea ice forcings were linearly interpolated to daily
values and applied as anomalies upon the mean seasonal cycle.
Figure 1 shows the monthly SST and sea ice anomaly
forcing fields during December–April, the period of themodel experiments. The SST forcing field exhibits
some evolution over the course of the winter. In December–February, the main anomaly is positive and located in the subpolar gyre, with a maximum amplitudeof ⬃7 K in February; a weaker negative anomaly isfound off the east coast of North America (maximum
amplitude 3 K). In late winter (March–April), the two
anomaly centers are approximately equal in amplitude
and area extent. The sea ice forcing is nearly constantover the course of the winter, consisting of a reduction
in sea ice cover in the Greenland Sea and an extensionof the ice edge in the Labrador Sea.
Two sets of anomaly experiments were conducted:
one in which CCM3 is forced with the SST anomalies

4754

JOURNAL OF CLIMATEand the sea ice extent is held fixed at its seasonallyvarying climatological values, and one in which themodel is forced with the sea ice anomalies and the SST
field is set to its climatological seasonal cycle. Each setof experiments, hereafter referred to as “SST” and“ICE,” consists of a 240-member ensemble of 5-monthintegrations starting on 1 December and ending on 30
April. The atmospheric initial conditions are different
for each ensemble member and were obtained fromlong control runs with no anomalous forcing. In addition to the two sets of anomaly experiments, a 240member control ensemble was conducted with noanomalies applied to either the SST or sea ice fields.
The initial conditions for this control ensemble are
identical to those used in the anomaly experiments.
Thus, one can think of the experiments as consisting ofa pair of integrations (one with and one without boundary forcing), with each member of the pair starting fromthe same initial conditions, but different initial conditions used for each of the 240 pairs.
To obtain the atmospheric response to the prescribed
SST or sea ice anomaly forcing, the control experiment
was subtracted from its paired anomaly experiment foreach day of the integration; the daily ensemble mean
response was then defined by averaging the 240 individual member responses. Statistical significance wasassessed by means of a two-sided Student’s t test.
As stated in the introduction, we used the NCAR
CCM3 in the standard configuration as detailed in
Kiehl et al. (1998) to be consistent with the earlier experiments of D04. CCM3 has a horizontal resolution of
approximately 2.8° latitude and 2.8° longitude (T42
spectral truncation) with 18 vertical layers in a hybridsigma coordinate system. The reader is referred to
Hack et al. (1998) and Hurrell et al. (1998) for assessments of the hydrologic, thermodynamic, and dynamical characteristics of CCM3.

b. Linear baroclinic model
To understand the mechanisms for the CCM3 atmospheric circulation response to SST or sea ice anomalies, we force a linear baroclinic model (LBM) with the
diabatic heating and transient eddy heat and vorticity
fluxes from the SST and ICE GCM experiments. The
LBM, the same as that used in Peng et al. (2003), isbased on the primitive equations configured with T21
horizontal resolution and 10 equally spaced pressurelevels from 950 to 50 hPa. The model is linearized about
the CCM3 basic state based on the mean of the 240
control integrations. Two periods (1 December–15
January and 15 January–15 April) are used for defining
the basic state since the evolution of the basic state overthe course of the winter may impact the response to

VOLUME 20

anomalous forcing; however, sensitivity experimentswith the basic state for different periods indicate thechange in the LBM results presented here is small (notshown). The LBM includes dissipation in the form ofRayleigh friction in the momentum equations and Newtonian cooling in the thermodynamic equation, as wellas biharmonic thermal diffusion. The Rayleigh andNewtonian damping time scales are 1 day at 950 hPatransitioning linearly to 7 days above 700 hPa, identicalto those used in Peng et al. We have repeated all of theanalyses with a damping time scale of 10 days above 700hPa (not shown), with the result that the LBM responses are slightly stronger with the reduced damping,as expected; however, given the arbitrary nature of thedissipation formulation, we have chosen not to tune
these coefficients. The LBM is integrated for 50 days,
having reached a steady solution by ⬃15–20 days (notshown). Note that the LBM is being used in a diagnostic
sense to determine the relative contributions of diabatic
heating and transient eddy heat and vorticity fluxes tothe dynamical maintenance of the response, not in a
prognostic sense (e.g., the time scale of the LBM response is irrelevant to our purpose).
The maintenance of the CCM3 response to SST and
sea ice forcing is diagnosed by comparing the LBM
responses to the anomalous diabatic heating and transient eddy heat and vorticity fluxes from the SST and
ICE experiments. Transient eddies are defined by subtracting 11-day running means from the raw daily data(the half-power point of this filter is 14 days). To avoid
loss of information at the end points of the integrations,the transients for the first and last week are defined asdaily departures from the time means of the first andlast week, respectively.

3. Resultsa. Geopotential height response
The first 5 days (2–6 December) of the ensemble
mean geopotential height responses at 1000 and 300
hPa are shown in Fig. 2 for the SST and ICE experiments (note that 2 December is the first full day of theintegrations). The responses are localized to the vicinityof the boundary forcing, particularly at 1000 hPa, andexhibit a baroclinic structure in the vertical, with ananomalous surface low (high) over positive (negative)
surface temperature anomalies, accompanied by ananomalous ridge (trough) aloft. This response amplifies
with time, reaching maximum values ⬃40 m at 300 hPa
on 6 December. The responses are statistically significant at the 95% confidence level based on a Student’s ttest, as indicated by the shaded regions in Fig. 2.
Figures 3a,b show the full evolution of the weekly

15 SEPTEMBER 2007

DESER ET AL.

4755

FIG. 2. Ensemble-mean 1000- and 300-hPa geopotential height responses for the first 5 days of the (top) SST and (bottom) ICE
experiments. The contour interval is 10 m, with solid (dashed) contours denoting positive (negative) values; the zero contour has been
omitted for clarity. Shading denotes values significant at the 95% confidence level based on a two-sided Student’s t test.

averaged ensemble mean geopotential height responses
at 1000 and 300 hPa over the course of the winter (December–April) for the SST and ICE experiments, respectively. To fit all the weeks onto a single diagram,
every other week is omitted starting in February. The
initial localized baroclinic response evolves into a nearhemispheric equivalent barotropic pattern after approximately 6–7 weeks. This hemispheric pattern ismaintained for most of the remaining winter, although
individual anomaly centers fluctuate in their amplitude
and location. The initial localized response pattern may
still be seen embedded within the larger-scale responseat 1000 hPa, especially in the ICE case. Once the hemispheric equivalent barotropic pattern becomes estab-lished (early–mid-January), the SST and ICE responses
are similar in their gross features especially at 300 hPa,
with positive anomalies over high latitudes and negative anomalies in midlatitudes. However, the negative
anomaly over the North Atlantic tends to be locatedfarther poleward in the ICE case compared to the SST
case, with a concurrent shift in the positive anomaly
center to the north. This poleward shift is in keepingwith the northward shift of the location of the forcing in
the two experiments (cf. Fig. 1). The response over the
North Pacific is weaker in the ICE case than the SST
case during late January–mid-February.
The vertical structure of the geopotential height response as a function of time is summarized in Fig. 4,

4756

JOURNAL OF CLIMATEVOLUME 20

FIG. 3. As in Fig. 2, but for weekly averages over the course of the integrations (1 Dec– 30 Apr). The dates correspond to the midpointof the weekly average (e.g., 5 Dec refers to the week of 2–8 Dec). Note that, starting in February, only every other week is shown due
to space limitations. (a) SST experiment; (b) ICE experiment.

which shows the spatial correlation coefficients between daily anomaly fields at 1000 and 300 hPa north of
30°N (the anomalies have been multiplied by thesquare root of the cosine of latitude before computing
the correlation to ensure proper area weighting). A
3-day running mean was applied to the daily anomaly

fields before computing the spatial correlations tosmooth the results slightly. Both the SST and ICE experiments exhibit strong negative spatial correlation coefficients within the first week, consistent with theanomaly maps shown in Fig. 3; the magnitudes of thenegative correlations are larger in the SST case than the

15 SEPTEMBER 2007

DESER ET AL.

4757

FIG. 3. (Continued)

ICE case (cf. 0.82 and 0.47) due to the larger spatial
scale of the SST forcing compared to the sea ice forcing.
The correlation coefficients change sign from negative
to positive values (e.g., indicative of a transition from abaroclinic to an equivalent barotropic vertical structure) during the third week (the transition occurs a few
days later for SST than ICE). The positive spatial cor-

relations reach similar maximum values (⬃0.7) from
mid-January to the end of March in the SST and ICE
cases; in April, the positive correlations have weakened
in the SST case, but remain high in the ICE case.
The overall amplitude of the daily geopotential
height responses at 1000 and 300 hPa as a function oftime are summarized in Fig. 5, which shows the daily

4758

JOURNAL OF CLIMATEVOLUME 20

FIG. 4. Spatial correlation coefficients between daily geopotential height responses at 1000 and 300 hPa for the SST and ICE
experiments. The daily data have been smoothed with a 3-day
running mean filter before computing the correlations. The top
and bottom horizontal axes are labeled according to week number(week 1 refers to 2–8 Dec) and calendar day, respectively.

rms values of the area-weighted anomalies averaged
over the Northern Hemisphere poleward of 30°N. The
rms values are approximately 3 times as large at 300 as
at 1000 hPa and attain their maxima after approximately 8 weeks in the SST case and 10 weeks in the ICE
case.

b. Role of internal variability
Following D04, we examine the relationship between
the leading mode of internal variability in the control
ensemble and the ensemble mean response to the imposed boundary forcing in SST and ICE. The initial(days 1–14) and time-averaged (days 15–150) geopotential height responses at 1000, 650, and 300 hPa in SST
and ICE are shown in Fig. 6 (columns 1 and 2). As
already noted with regard to Fig. 3, the initial responses
are baroclinic and localized to the vicinity of the forcing
while the time averaged responses are equivalent barotropic and hemispheric in scale, with positive anomalies
at high latitudes and negative anomalies at midlatitudes
over the Atlantic and Pacific. Removing the initial response from the time-averaged response (Fig. 6, column 3) at each level yields patterns that resemble
closely the leading EOF of time-averaged geopotential
height anomalies in the 240-member control ensemble(Fig. 6, column 4), with spatial correlation coefficientsof 0.86–0.89 for SST and 0.93–0.94 for ICE depending
on level. (The leading EOF of the control ensemble iscomputed separately for each level using the 240 time-

FIG. 5. Amplitudes of the daily geopotential height responses at1000 and 300 hPa for the (top) SST and (bottom) ICE experiments. Amplitudes are defined as the area average of the rms of
the height responses north of 30°N.

averaged geopotential height fields, and accounts for
32%–39% of the variance depending on level.) Notethat the EOF patterns in Fig. 6 have been scaled forvisual convenience by a spatial regression coefficientobtained separately for each level by projecting the pattern in column 3 (time-averaged minus initial response)
onto the unscaled EOF.
Subtracting EOF1 of the control ensemble (Fig. 6,
column 4) from the time-averaged response (Fig. 6, column 2) yields a residual pattern (Fig. 6, column 5) that
resembles the initial response, especially in the ICE
case. However, it is also apparent that the amplitude of
the residual response in the mid- and upper troposphere is larger than that of the initial response. The
time-averaged responses, EOF structures, and residual
patterns obtained from the transient experiments (Fig.
6) are qualitatively similar to those from the equilibrium experiments shown in D04.
At what point in the transient integrations does the15 SEPTEMBER 2007

DESER ET AL.

4759

FIG. 6. Geopotential height responses at 1000, 650, and 300 hPa for the (top) SST and (bottom) ICE experiments. The contour
interval in all panels is 10 m, with solid (dashed) contours denoting positive (negative) values; the zero contours have been omitted forclarity. “Initial” refers to the response averaged over the first 2 weeks (days 1–14); “Time Avg” refers to the time mean response after
the second week (days 15–150); “Time Avg-Init” refers to “Time Avg” minus “Initial”; “EOF1 Control” refers to the leading EOF ofthe time mean of the 240 control experiments, scaled as described in the text; “Time Avg – EOF1” refers to “Time Avg” minus “EOF1Control”.

4760

JOURNAL OF CLIMATEVOLUME 20

integration, from values ⬃0.4 in early December to⬃0.85 in late April. The higher correlation coefficientsfor ICE than SST in the first 6 weeks at 300 hPa reflectthat the centers of action of the response in ICE arecontained within those of the EOF pattern. The spatial
correlation coefficients for the ICE case are considerably weaker at 1000 hPa compared to 300 hPa during
the first 9 weeks due to the stronger localized response
directly over the sea ice anomalies compared to theremote large-scale response.

c. Roles of diabatic heating and transient eddies

FIG. 7. Spatial correlation coefficients between the daily geopotential height responses and the leading EOF of the time-meancontrol ensemble at 1000 and 300 hPa, based on data north of
30°N for the (top) SST and (bottom) ICE experiments. The daily
data were smoothed with a 3-point running mean before computing the spatial correlations.

response begin to resemble the leading EOF of thecontrol ensemble? To answer this question, we havecomputed the daily spatial correlation coefficient between the ensemble-mean geopotential height response
and the control ensemble EOF1 at 1000 and 300 hPa
based on area-weighted anomalies north of 30°N (Fig.
7). Daily values of EOF1 were obtained by computing
the leading EOF for each month separately and thenlinearly interpolating the monthly values to daily values. Using a single EOF1 pattern based on December–
April averages yields very similar results (not shown).
For the SST case, the response starts to resemble EOF1
around week 7 (mid-January) and continues throughweek 17 (late March), with spatial correlation coefficients ⬃0.7–0.9. The correlation coefficients diminish in
April, with values ⬃0.6 at 300 hPa and ⬃0.4 at 1000
hPa. For the ICE case, the correlation coefficients at
the 300-hPa level increase through the course of the

We use the LBM of Peng et al. (2003) to diagnose themaintenance of the CCM3 response by anomalous diabatic heating and transient eddy heat and vorticity
fluxes in the SST and ICE experiments. Figure 8 shows
the LBM responses to diabatic heating and transient
eddy fluxes in week 1, along with the corresponding
CCM3 response. Note that the contour interval for the
LBM responses is 5 m compared to 10 m for the GCM
response. The spatial and vertical structure of the LBM
response to diabatic heating is similar to the full GCM
response, especially for the ICE case, although the amplitude is approximately a factor of 2 smaller in themid- and upper troposphere. Peng et al. (2003) also
found that the LBM underestimates the amplitude of
the GCM by approximately a factor of 2, and attributed
this shortcoming to limitations of the linear dynamics,
damping prescriptions, and differences in the model
configurations (note that we have not attempted totune the dissipation parameters). The LBM response totransient eddy heat and vorticity fluxes is small compared to the response to diabatic heating at the initialstage.
The diabatic heating anomalies used to force the
LBM (Fig. 9) reflect the locations of the SST and sea ice
anomalies, and are largest in the lower troposphere.
Note that the anomalous heating profile is shallower for
sea ice than for SST, consistent with the vertical structures of the geopotential height responses shown in Fig.
8. Although the LBM captures the baroclinic structureof the GCM response, the transition between negativeand positive anomalies occurs at a slightly higher altitude in the LBM than in the GCM, likely related to thedifferent vertical resolutions of the two models.
Figure 10 shows the LBM responses to diabatic heating and transient eddy heat and vorticity fluxes for theperiod 15 January–1 April when the GCM response
pattern is fully developed (the results are not sensitive
to the precise choice of averaging period once the hemispheric character of the response has been established).
The LBM response to transient eddy forcing captures
much of the hemispheric, equivalent barotropic char-15 SEPTEMBER 2007

DESER ET AL.

FIG. 8. LBM responses at 350, 650, and 950 hPa to diabatic
heating [LBM(Q); left column) and transient eddy heat and vorticity fluxes [LBM(TE); right column) from the CCM3 SST and
ICE experiments averaged over the first week. The full CCM3
responses averaged over the first week are shown in the middle
column (labeled GCM). The contour interval for the LBM(CCM3) responses is 5 m (10 m), with solid (dashed) contours
denoting positive (negative) values; the zero contours have been
omitted for clarity. See text for details of the LBM calculations.

acter of the full GCM response in both the SST and
ICE cases, although as before, the amplitude of the
LBM response is approximately a factor of 2 smaller
than the full GCM response. The vorticity componentof the transient eddy fluxes dominates the heating com-4761

ponent in terms of the LBM response, although at 350
hPa, transient eddy heat fluxes substantially augment
the transient eddy vorticity fluxes (Fig. 10, columns 4
and 5). These results are in keeping with previous studies regarding the relative importance of transient eddy
heat and vorticity fluxes (Ting and Lau 1993).
The LBM response to diabatic heating is small compared to the response to transient eddy fluxes, and issimilar in structure to that in week 1 but ⬃30% smallerin amplitude (cf. Fig. 8). The weakening of the LBM
response to diabatic heating over time reflects a reduction of the anomalous diabatic heating in the lower
troposphere associated with the thermal adjustment of
the atmospheric planetary boundary layer to the underlying surface temperature anomaly as shown in Fig. 9.
The structure of the transient eddy vorticity flux convergence anomalies used to force the LBM (Fig. 11)
corresponds approximately to that of the geopotential
height response in the GCM and maximizes in the upper troposphere, consistent with previous studies (e.g.,
Lau and Holopainen 1984).
The temporal evolution of the contributions of diabatic heating and transient eddy heat and vorticity
fluxes as diagnosed from the LBM to the full CCM3
response is summarized in Fig. 12. This diagram shows
the spatial correlation and regression coefficients between the actual GCM response and the LBM responses to diabatic heating, transient eddy heat, andvorticity fluxes based upon weekly averages smoothedwith a 1–2–1 binomial filter. For the SST case at 950
hPa, the contribution by diabatic heating exceeds thatby transient eddy fluxes during the first 3 weeks, withspatial correlations near or exceeding 0.8. The contribution by diabatic heating gradually decays to near zeroby the 10th week, while the contribution by transient
eddy forcing dominates between weeks 5 and 16, withspatial correlation coefficients near 0.7 and spatial regression coefficients near 0.4. At 350 hPa, the contribution by transient eddies dominates that by diabatic
heating by the third week of the simulations, with spatial correlation coefficients near 0.8 and regressions
near 0.4. (In fact, the LBM response to diabatic heating
at 350 hPa is negatively correlated with the GCM response between weeks 5 and 16.)
The results for the ICE case are similar to those for
SST, especially at 350 hPa. However, at 950 hPa during
the first two weeks of the simulations, the spatial correlation and regression coefficients between the LBM
response to transient eddy fluxes and the GCM response are positive, opposite to the SST case, and thecorrespondence between the LBM response to diabatic
heating and the GCM response is lower than that in the4762

JOURNAL OF CLIMATE

FIG. 9. (left) Vertically averaged diabatic heating responses fordays 2–8 and 45–120 for the (top) SST and (bottom) ICE experiments. The contour interval is 0.2 K day⫺1 for SST and 0.4 K
day⫺1 for ICE. Solid (dashed) contours denote positive (negative)
values, with the zero contours omitted. (right)Vertical profiles ofthe diabatic heating responses for days 2–8 (thin curve) and days
45–120 (thick curve) averaged over the region of positive anomalies (50°–70°N, 60°–15°W for SST and 60°–90°N, 45°W–120°E forICE).

SST case, perhaps due to the more limited areal extent
of the sea ice forcing relative to SST forcing (cf. Fig. 1).

4. Summary and discussion
We have investigated the transient evolution of thewinter atmospheric circulation response to prescribed

VOLUME 20

North Atlantic SST and sea ice extent anomalies using
CCM3, an atmospheric general circulation model withT42 horizontal resolution and 18 levels in the vertical.
Three 240-member ensemble integrations were conducted, all beginning on 1 December and ending on 30
April: a control integration in which SST and sea ice
extent are set to their observed climatological seasonally varying values, an SST perturbation integration inwhich a seasonally varying pattern of SST anomalies is
added to the mean SST field, and a sea ice perturbation
integration in which a seasonally varying pattern of sea
ice extent anomalies is added to the mean sea ice extent
field. Different atmospheric initial conditions are used
for each member of the ensembles. The SST and sea ice
extent anomaly patterns are identical to those used in
the equilibrium response studies of M04 and D04, andare based upon observed trends during the second halfof the twentieth century. Following M04 and D04, theamplitudes of the SST (sea ice) anomalies were increased by a factor of 5 (⬃2) to enhance the signal-tonoise ratios of the responses, and the polarity of the
SST anomalies was reversed to facilitate comparison
between the atmospheric circulation responses to thetwo types of boundary forcing. The maintenance of thetransient atmospheric response at different stages of its
evolution was diagnosed by means of a linear primitive
equation model forced with the diabatic heating andtransient eddy heat and vorticity flux anomalies fromthe GCM experiments.
The initial adjustment of the atmospheric circulation
to the SST or sea ice extent forcing is characterized byan out-of-phase relationship between geopotential
height anomalies in the lower and upper tropospherelocalized to the vicinity of the forcing. This initial baroclinic response reaches maximum amplitude in ⬃5–10
days (not shown), and persists for 2–3 weeks. Diagnostic results from the linear primitive equation model indicate that this component of the response is forced bydiabatic heating anomalies in the lower troposphere associated with surface heat flux anomalies generated by
the imposed boundary forcing. This mechanism is consistent with that described by the time-independent linear model of Hoskins and Karoly (1981). The 5–10-day
adjustment time for the initial baroclinic response
agrees well with that found in the single-column modelexperiments of Chase et al. (2002) and Tsukernik et al.
(2004) in which a polar atmospheric column is subjected to open water heating or surface cooling.
Following the initial baroclinic stage of adjustment,
the response becomes progressively more barotropic
and increases in both spatial extent and magnitude. The
equilibrium stage of adjustment is reached in 2–2.5
months, and is characterized by an equivalent barotro-

15 SEPTEMBER 2007

DESER ET AL.

4763

FIG. 10. As in Fig. 8, but for the average of days 45–120. The 4th and 5th columns show the. LBM responses to transient eddy flux
convergences of vorticity [LBM(TE_vor)] and heat [LBM(TE_heat)].

4764

JOURNAL OF CLIMATE

FIG. 11. (left) Vertically averaged transient eddy vorticity flux
convergence response averaged over days 45–120 for the (top)
SST and (bottom) ICE experiments. The contour interval is 0.8
m2 s⫺2. Solid (dashed) contours denote positive (negative) values,
with the zero contours omitted. (right) Vertical profiles of thetransient eddy vorticity flux convergence responses for days 45–
120 averaged north of 55°N from the SST experiment (thin curve)
and ICE experiment (thick curve).

pic structure that resembles the hemispheric NAO–
NAM pattern, the model’s leading internal mode ofcirculation variability over the Northern Hemisphere.
The maximum amplitude of the equilibrium response isapproximately 2–3 times larger than that of the initial
response. The equilibrium response is maintained primarily by nonlinear transient eddy fluxes of vorticity(and, to a lesser extent, heat), with diabatic heating
making a limited contribution in the vicinity of the forcing. The maintenance of the equilibrium geopotential
height response by transient eddy vorticity fluxes is
consistent with numerous earlier studies (e.g., Kushnir
and Lau 1992; Ting and Peng 1995; Peng and Whitaker
1999; Peng et al. 2003).
The evolution of the transient response follows themechanistic scenario put forth by Peng and Whitaker(1999), and is qualitatively similar to the transient adjustment process obtained in a three-level quasigeostrophic model by Ferreira and Frankignoul (2005).
The 2–2.5-month time scale for the establishment of theequilibrium NAO–NAM response in this study isshorter than the 3–4 months obtained by Ferreira andFrankignoul (2005), although we note that both areconsiderably longer than the ⬃10 days that are generally assumed. A number of factors may contribute to
the different adjustment times for the NAO–NAM in

VOLUME 20

our study and that of Ferreira and Frankignoul, including: different types of atmospheric models (18-level
GCM at T42 resolution versus three-layer quasigeostrophic model at T31 resolution); different North Atlantic SST forcing patterns; and different treatments ofthe seasonal cycle (seasonally varying conditions versus
perpetual December–February conditions).
We cannot directly address the issue of how seasonalchanges in the background state, in particular the location and strength of the storm tracks, may impact theadjustment time, amplitude, and pattern of the equilibrium response in CCM3 without additional experiments
in which the background state is kept fixed. However,
we note that the North Atlantic and Pacific storm
tracks are already well established by the first week ofDecember, the starting date for our experiments (notshown). Had our experiments begun a month earlier,we speculate that the adjustment time for the NAO–
NAM response might have been longer consideringthat the transient eddy–mean flow interactions arelikely to be weaker in November than December. On
the other hand, the reduction of the NAO–NAM response in April compared to February and March islikely to be in part a consequence of the seasonal weakening of the storm track, especially in the sea ice experiment where the forcing does not change appreciably over the course of the integrations (cf. Fig. 1).
One of the motivations for the current study was toinvestigate the physical basis for D04’s empirical decomposition of the equilibrium response into a component that projects onto the model’s leading mode of
intrinsic variability (termed the “indirect response”)
and a component that is the residual from that projection (termed the “direct response”). Based upon the
different spatial and vertical structures of these components, D04 speculated that transient eddy forcing maintains the indirect response while diabatic heating maintains the direct response. Performing the same empirical decomposition on the time-averaged transient
response, we found similar patterns for the direct andindirect responses as D04. We further noted that thedirect response resembles the initial transient response.
Thus, the direct and indirect responses are largely consistent with D04’s physical interpretation by virtue of
their similarity to the initial and equilibrium responses,
respectively. However, transient eddy forcing may also
contribute to the direct response, especially in the midand upper troposphere where the direct response islarger than the initial response. Thus, the empirical decomposition technique of D04, while physically insightful, may not fully isolate the diabatically forced andtransient eddy-forced components of the response.

15 SEPTEMBER 2007

DESER ET AL.

4765

FIG. 12. (left) Spatial correlation and (right) regression coefficients between the full CCM3 andthe LBM responses to diabatic heating (Q) and transient eddy heat and vorticity fluxes (TE)based upon weekly averages smoothed with a 1–2–1 binomial filter for the (top) SST and (bottom) ICE experiments. Spatial correlation/regression coefficients are based upon geopotential
height responses at 350 hPa and 950 hPa. See text for details.

As discussed in M04 and D04, the atmospheric circulation response to the imposed sea ice anomaly trend
constitutes a moderate (40%) negative feedback: it is
opposite in sign to, and approximately 40% of the magnitude of, the atmospheric circulation anomaly patternthat originally forced the sea ice anomaly trend. The

sign of this feedback is maintained in an additional integration of the model in which both the observed SST
and sea ice anomaly trends are jointly imposed, eventhough the atmospheric response to the observed SST
trend represents a positive (but weak) feedback (seeM04 and D04). Since the amplitude of the atmospheric4766

JOURNAL OF CLIMATE

response was found to scale approximately linearly withthe amplitude of the SST or sea ice forcing for a given
forcing polarity (M04 and D04), the magnitude of thisfeedback is likely to be similar for more realistic forcing
amplitudes than those used here.
The 2–2.5-month adjustment time scale of the NAO–
NAM to imposed patterns of North Atlantic SST and
sea ice anomalies found in this study is comparable tothe persistence time scale of the SST and sea ice
anomalies themselves (Frankignoul and Hasselmann
1977; Deser et al. 2003). Thus, the extratropical atmosphere and ocean mixed layer (or sea ice) should beviewed as continually and mutually adjusting systems
over this time scale. A recent modeling study by Cassou
et al. (2007) examining the mutual transient adjustmentof the atmosphere and ocean mixed layer to a subsurface ocean temperature anomaly in the extratropicalNorth Atlantic supports this view.
The results obtained in this study pertain to one
model (CCM3) forced with one particular pattern ofNorth Atlantic SST (or sea ice) anomalies. It is likelythat the time scale for the establishment of the equilibrium atmospheric response, as well as its amplitude,
pattern, and duration may vary considerably with themodel and forcing characteristics. Additional experiments with different forcing patterns and different atmospheric models, as well as coupled ocean–
atmosphere models, are needed to further our understanding of the equilibrium atmospheric response to
extratropical SST and sea ice anomalies.
Acknowledgments. Support from NOAA’s CLIVAR
Program to C. Deser is gratefully acknowledged. We
thank the three reviewers for their constructive andthoughtful comments that helped to improve the manuscript.
REFERENCES
Branstator, G., 1992: The maintenance of low frequency atmospheric anomalies. J. Atmos. Sci., 49, 1924–1945.
Cassou, C., C. Deser, and M. A. Alexander, 2007: Investigating
the impact of reemerging sea surface temperature anomalieson the winter atmospheric circulation over the North Atlantic. J. Climate, 20, 3510–3526.
Chase, T. N., B. Herman, R. A. Pielke Sr., X. Zeng, and M.
Leuthold, 2002: A proposed mechanism for the regulation ofminimum mid-tropospheric temperatures in the Arctic. J.
Geophys. Res., 107, 4193, doi:10.1029/2001JD001425.
Deser, C., J. E. Walsh, and M. S. Timlin, 2000: Arctic sea ice
variability in the context of recent atmospheric circulation
trends. J. Climate, 13, 617–633.
——, M. A. Alexander, and M. S. Timlin, 2003: Understanding
the persistence of sea surface temperature anomalies in midlatitudes. J. Climate, 16, 57–72.

VOLUME 20——, G. Magnusdottir, R. Saravanan, and A. S. Phillips, 2004: The
effects of North Atlantic SST and sea-ice anomalies on thewinter circulation in CCM3. Part II: Direct and indirect components of the response. J. Climate, 17, 877–889.
Ferreira, D., and C. Frankignoul, 2005: The transient atmospheric
response to midlatitude SST anomalies. J. Climate, 18, 1049–
1067.
Frankignoul, C., and K. Hasselmann, 1977: Stochastic climate
models. Part 2: Application to sea-surface temperature variability and thermocline variability. Tellus, 29, 289–305.
Hack, J. J., J. T. Kiehl, and J. W. Hurrell, 1998: The hydrologic
and thermodynamic characteristics of the NCAR CCM3. J.
Climate, 11, 1179–1206.
Hall, N. M. J., J. Derome, and H. Lin, 2001: The extratropicalsignal generated by a midlatitude SST anomaly. Part I: Sensitivity at equilibrium. J. Climate, 14, 2035–2053.
Hendon, J. J., and D. L. Hartmann, 1982: Stationary waves on asphere: Sensitivity to thermal feedback. J. Atmos. Sci., 39,
1906–1920.
Hoskins, B. J., and D. Karoly, 1981: The steady linear response ofa spherical atmosphere to thermal and orographic forcing. J.
Atmos. Sci., 38, 1179–1196.
Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation. Science, 269,
676–679.
——, J. J. Hack, B. A. Boville, D. L. Williamson, and J. T. Kiehl,
1998: The dynamical simulation of the NCAR Community
Climate Model version 3 (CCM3). J. Climate, 11, 1207–1236.
Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, D. L. Williamson, and P. J. Rasch, 1998: The National Center for Atmospheric Research community climate model: CCM3. J. Climate, 11, 1131–1149.
Kushnir, Y., and N.-C. Lau, 1992: The general circulation model
response to a North Pacific SST anomaly: Dependence ontime scale and pattern polarity. J. Climate, 5, 271–283.
——, W. A. Robinson, I. Blade, N. M. J. Hall, S. Peng, and R.
Sutton, 2002: Atmospheric GCM response to extratropical
SST anomalies: Synthesis and evaluation. J. Climate, 15,2233–2256.
Lau, N.-C., and E. O. Holopainen, 1984: Transient eddy forcing of
the time-mean flow as identified by geopotential tendencies.
J. Atmos. Sci., 41, 313–328.
Li, Z. X., 1999: Ensemble atmospheric GCM simulation of climate
interannual variability from 1979 to 1994. J. Climate, 12, 986–
1001.
——, and S. Conil, 2003: Transient response of an atmospheric
GCM to North Atlantic SST anomalies. J. Climate, 16, 3993–
3998.
Magnusdottir, G., C. Deser, and R. Saravanan, 2004: The effectsof North Atlantic SST and sea-ice anomalies on the wintercirculation in CCM3. Part I: Main features and storm-track
characteristics of the response. J. Climate, 17, 857–876.
Peng, S., and J. S. Whitaker, 1999: Mechanisms determining theatmospheric response to midlatitude SST anomalies. J. Climate, 12, 1393–1408.
——, and W. A. Robinson, 2001: Relationships between atmospheric internal variability and the responses to an extratropical SST anomaly. J. Climate, 14, 2943–2959.
——, ——, and S. Li, 2003: Mechanisms for the NAO responses to
the North Atlantic SST tripole. J. Climate, 16, 1987–2004.
Raynor, N. A., E. B. Horton, D. E. Parker, C. K. Folland, and15 SEPTEMBER 2007

DESER ET AL.

R. B. Hackett, 1996: Version 2.3 of the global sea ice and sea
surface temperature data set 1903–1994. Hadley Centre Tech.
Note CRTN 74, 21 pp. [Available from Hadley Centre for
Climate Prediction and Research, London Rd., Bracknell,
Berkshire RG12 2SY, United Kingdom.]
Robinson, W. A., 2000: Review of WETS—The workshop on extratropical SST anomalies. Bull. Amer. Meteor. Soc., 81, 567–
577.
Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height andtemperature fields. Geophys. Res. Lett., 25, 1297–1300.
Ting, M., and N.-C. Lau, 1993: A diagnostic and modeling study of4767

the monthly mean wintertime anomalies appearing in a 100year GCM experiment. J. Atmos. Sci., 50, 2845–2867.
——, and S. Peng, 1995: Dynamics of the early and middle winteratmospheric responses to northwest Atlantic SST anomalies.
J. Climate, 8, 2239–2254.
Tsukernik, M., T. N. Chase, M. C. Serreze, R. G. Barry, R. Pielke
Sr., B. Herman, and X. Zeng, 2004: On the regulation ofminimum mid-tropospheric temperatures in the Arctic. Geophys. Res. Lett., 31, L06112, doi:10.1029/2003GL018831.
Walter, K., U. Luksch, and K. Fraedrich, 2001: A response climatology of idealized midlatitude thermal forcing experimentswith and without a storm track. J. Climate, 14, 467–484.